2015 Marshall Urist Young Investigator Award: Prognostication in Patients With Long Bone Metastases: Does a Boosting Algorithm Improve Survival Estimates?
Background Survival estimation guides surgical decision-making in metastatic bone disease. Traditionally, classic scoring systems, such as the Bauer score, provide survival estimates based on a summary score of prognostic factors. Identification of new factors might improve the accuracy of these mod...
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| Published in | Clinical orthopaedics and related research Vol. 473; no. 10; pp. 3112 - 3121 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.10.2015
Lippincott Williams & Wilkins Ovid Technologies |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0009-921X 1528-1132 1528-1132 |
| DOI | 10.1007/s11999-015-4446-z |
Cover
| Abstract | Background
Survival estimation guides surgical decision-making in metastatic bone disease. Traditionally, classic scoring systems, such as the Bauer score, provide survival estimates based on a summary score of prognostic factors. Identification of new factors might improve the accuracy of these models. Additionally, the use of different algorithms—nomograms or boosting algorithms—could further improve accuracy of prognostication relative to classic scoring systems. A nomogram is an extension of a classic scoring system and generates a more-individualized survival probability based on a patient’s set of characteristics using a figure. Boosting is a method that automatically trains to classify outcomes by applying classifiers (variables) in a sequential way and subsequently combines them. A boosting algorithm provides survival probabilities based on every possible combination of variables.
Questions/purposes
We wished to (1) assess factors independently associated with decreased survival in patients with metastatic long bone fractures and (2) compare the accuracy of a classic scoring system, nomogram, and boosting algorithms in predicting 30-, 90-, and 365-day survival.
Methods
We included all 927 patients in our retrospective study who underwent surgery for a metastatic long bone fracture at two institutions between January 1999 and December 2013. We included only the first procedure if patients underwent multiple surgical procedures or had more than one fracture. Median followup was 8 months (interquartile range, 3-25 months); 369 of 412 (90%) patients who where alive at 1 year were still in followup. Multivariable Cox regression analysis was used to identify clinical and laboratory factors independently associated with decreased survival. We created a classic scoring system, nomogram, and boosting algorithms based on identified variables. Accuracy of the algorithms was assessed using area under the curve analysis through fivefold cross validation.
Results
The following factors were associated with a decreased likelihood of survival after surgical treatment of a metastatic long bone fracture, after controlling for relevant confounding variables: older age (hazard ratio [HR], 1.0; 95% CI, 1.0–1.0; p < 0.001), additional comorbidity (HR, 1.2; 95% CI, 1.0–1.4; p = 0.034), BMI less than 18.5 kg/m
2
(HR, 2.0; 95% CI, 1.2–3.5; p = 0.011), tumor type with poor prognosis (HR, 1.8; 95% CI, 1.6–2.2; p < 0.001), multiple bone metastases (HR, 1.3; 95% CI, 1.1–1.6; p = 0.008), visceral metastases (HR, 1.6; 95% CI, 1.4–1.9; p < 0.001), and lower hemoglobin level (HR, 0.91; 95% CI, 0.87–0.96; p < 0.001). The survival estimates by the nomogram were moderately accurate for predicting 30-day (area under the curve [AUC], 0.72), 90-day (AUC, 0.75), and 365-day (AUC, 0.73) survival and remained stable after correcting for optimism through fivefold cross validation. Boosting algorithms were better predictors of survival on the training datasets, but decreased to a performance level comparable to the nomogram when applied on testing datasets for 30-day (AUC, 0.69), 90-day (AUC, 0.75), and 365-day (AUC, 0.72) survival prediction. Performance of the classic scoring system was lowest for all prediction periods.
Conclusions
Comorbidity status and BMI are newly identified factors associated with decreased survival and should be taken into account when estimating survival. Performance of the boosting algorithms and nomogram were comparable on the testing datasets. However, the nomogram is easier to apply and therefore more useful to aid surgical decision making in clinical practice.
Level of Evidence
Level III, prognostic study. |
|---|---|
| AbstractList | Background
Survival estimation guides surgical decision-making in metastatic bone disease. Traditionally, classic scoring systems, such as the Bauer score, provide survival estimates based on a summary score of prognostic factors. Identification of new factors might improve the accuracy of these models. Additionally, the use of different algorithms—nomograms or boosting algorithms—could further improve accuracy of prognostication relative to classic scoring systems. A nomogram is an extension of a classic scoring system and generates a more-individualized survival probability based on a patient’s set of characteristics using a figure. Boosting is a method that automatically trains to classify outcomes by applying classifiers (variables) in a sequential way and subsequently combines them. A boosting algorithm provides survival probabilities based on every possible combination of variables.
Questions/purposes
We wished to (1) assess factors independently associated with decreased survival in patients with metastatic long bone fractures and (2) compare the accuracy of a classic scoring system, nomogram, and boosting algorithms in predicting 30-, 90-, and 365-day survival.
Methods
We included all 927 patients in our retrospective study who underwent surgery for a metastatic long bone fracture at two institutions between January 1999 and December 2013. We included only the first procedure if patients underwent multiple surgical procedures or had more than one fracture. Median followup was 8 months (interquartile range, 3-25 months); 369 of 412 (90%) patients who where alive at 1 year were still in followup. Multivariable Cox regression analysis was used to identify clinical and laboratory factors independently associated with decreased survival. We created a classic scoring system, nomogram, and boosting algorithms based on identified variables. Accuracy of the algorithms was assessed using area under the curve analysis through fivefold cross validation.
Results
The following factors were associated with a decreased likelihood of survival after surgical treatment of a metastatic long bone fracture, after controlling for relevant confounding variables: older age (hazard ratio [HR], 1.0; 95% CI, 1.0–1.0; p < 0.001), additional comorbidity (HR, 1.2; 95% CI, 1.0–1.4; p = 0.034), BMI less than 18.5 kg/m
2
(HR, 2.0; 95% CI, 1.2–3.5; p = 0.011), tumor type with poor prognosis (HR, 1.8; 95% CI, 1.6–2.2; p < 0.001), multiple bone metastases (HR, 1.3; 95% CI, 1.1–1.6; p = 0.008), visceral metastases (HR, 1.6; 95% CI, 1.4–1.9; p < 0.001), and lower hemoglobin level (HR, 0.91; 95% CI, 0.87–0.96; p < 0.001). The survival estimates by the nomogram were moderately accurate for predicting 30-day (area under the curve [AUC], 0.72), 90-day (AUC, 0.75), and 365-day (AUC, 0.73) survival and remained stable after correcting for optimism through fivefold cross validation. Boosting algorithms were better predictors of survival on the training datasets, but decreased to a performance level comparable to the nomogram when applied on testing datasets for 30-day (AUC, 0.69), 90-day (AUC, 0.75), and 365-day (AUC, 0.72) survival prediction. Performance of the classic scoring system was lowest for all prediction periods.
Conclusions
Comorbidity status and BMI are newly identified factors associated with decreased survival and should be taken into account when estimating survival. Performance of the boosting algorithms and nomogram were comparable on the testing datasets. However, the nomogram is easier to apply and therefore more useful to aid surgical decision making in clinical practice.
Level of Evidence
Level III, prognostic study. Survival estimation guides surgical decision-making in metastatic bone disease. Traditionally, classic scoring systems, such as the Bauer score, provide survival estimates based on a summary score of prognostic factors. Identification of new factors might improve the accuracy of these models. Additionally, the use of different algorithms--nomograms or boosting algorithms--could further improve accuracy of prognostication relative to classic scoring systems. A nomogram is an extension of a classic scoring system and generates a more-individualized survival probability based on a patient's set of characteristics using a figure. Boosting is a method that automatically trains to classify outcomes by applying classifiers (variables) in a sequential way and subsequently combines them. A boosting algorithm provides survival probabilities based on every possible combination of variables.BACKGROUNDSurvival estimation guides surgical decision-making in metastatic bone disease. Traditionally, classic scoring systems, such as the Bauer score, provide survival estimates based on a summary score of prognostic factors. Identification of new factors might improve the accuracy of these models. Additionally, the use of different algorithms--nomograms or boosting algorithms--could further improve accuracy of prognostication relative to classic scoring systems. A nomogram is an extension of a classic scoring system and generates a more-individualized survival probability based on a patient's set of characteristics using a figure. Boosting is a method that automatically trains to classify outcomes by applying classifiers (variables) in a sequential way and subsequently combines them. A boosting algorithm provides survival probabilities based on every possible combination of variables.We wished to (1) assess factors independently associated with decreased survival in patients with metastatic long bone fractures and (2) compare the accuracy of a classic scoring system, nomogram, and boosting algorithms in predicting 30-, 90-, and 365-day survival.QUESTIONS/PURPOSESWe wished to (1) assess factors independently associated with decreased survival in patients with metastatic long bone fractures and (2) compare the accuracy of a classic scoring system, nomogram, and boosting algorithms in predicting 30-, 90-, and 365-day survival.We included all 927 patients in our retrospective study who underwent surgery for a metastatic long bone fracture at two institutions between January 1999 and December 2013. We included only the first procedure if patients underwent multiple surgical procedures or had more than one fracture. Median followup was 8 months (interquartile range, 3-25 months); 369 of 412 (90%) patients who where alive at 1 year were still in followup. Multivariable Cox regression analysis was used to identify clinical and laboratory factors independently associated with decreased survival. We created a classic scoring system, nomogram, and boosting algorithms based on identified variables. Accuracy of the algorithms was assessed using area under the curve analysis through fivefold cross validation.METHODSWe included all 927 patients in our retrospective study who underwent surgery for a metastatic long bone fracture at two institutions between January 1999 and December 2013. We included only the first procedure if patients underwent multiple surgical procedures or had more than one fracture. Median followup was 8 months (interquartile range, 3-25 months); 369 of 412 (90%) patients who where alive at 1 year were still in followup. Multivariable Cox regression analysis was used to identify clinical and laboratory factors independently associated with decreased survival. We created a classic scoring system, nomogram, and boosting algorithms based on identified variables. Accuracy of the algorithms was assessed using area under the curve analysis through fivefold cross validation.The following factors were associated with a decreased likelihood of survival after surgical treatment of a metastatic long bone fracture, after controlling for relevant confounding variables: older age (hazard ratio [HR], 1.0; 95% CI, 1.0-1.0; p < 0.001), additional comorbidity (HR, 1.2; 95% CI, 1.0-1.4; p = 0.034), BMI less than 18.5 kg/m(2) (HR, 2.0; 95% CI, 1.2-3.5; p = 0.011), tumor type with poor prognosis (HR, 1.8; 95% CI, 1.6-2.2; p < 0.001), multiple bone metastases (HR, 1.3; 95% CI, 1.1-1.6; p = 0.008), visceral metastases (HR, 1.6; 95% CI, 1.4-1.9; p < 0.001), and lower hemoglobin level (HR, 0.91; 95% CI, 0.87-0.96; p < 0.001). The survival estimates by the nomogram were moderately accurate for predicting 30-day (area under the curve [AUC], 0.72), 90-day (AUC, 0.75), and 365-day (AUC, 0.73) survival and remained stable after correcting for optimism through fivefold cross validation. Boosting algorithms were better predictors of survival on the training datasets, but decreased to a performance level comparable to the nomogram when applied on testing datasets for 30-day (AUC, 0.69), 90-day (AUC, 0.75), and 365-day (AUC, 0.72) survival prediction. Performance of the classic scoring system was lowest for all prediction periods.RESULTSThe following factors were associated with a decreased likelihood of survival after surgical treatment of a metastatic long bone fracture, after controlling for relevant confounding variables: older age (hazard ratio [HR], 1.0; 95% CI, 1.0-1.0; p < 0.001), additional comorbidity (HR, 1.2; 95% CI, 1.0-1.4; p = 0.034), BMI less than 18.5 kg/m(2) (HR, 2.0; 95% CI, 1.2-3.5; p = 0.011), tumor type with poor prognosis (HR, 1.8; 95% CI, 1.6-2.2; p < 0.001), multiple bone metastases (HR, 1.3; 95% CI, 1.1-1.6; p = 0.008), visceral metastases (HR, 1.6; 95% CI, 1.4-1.9; p < 0.001), and lower hemoglobin level (HR, 0.91; 95% CI, 0.87-0.96; p < 0.001). The survival estimates by the nomogram were moderately accurate for predicting 30-day (area under the curve [AUC], 0.72), 90-day (AUC, 0.75), and 365-day (AUC, 0.73) survival and remained stable after correcting for optimism through fivefold cross validation. Boosting algorithms were better predictors of survival on the training datasets, but decreased to a performance level comparable to the nomogram when applied on testing datasets for 30-day (AUC, 0.69), 90-day (AUC, 0.75), and 365-day (AUC, 0.72) survival prediction. Performance of the classic scoring system was lowest for all prediction periods.Comorbidity status and BMI are newly identified factors associated with decreased survival and should be taken into account when estimating survival. Performance of the boosting algorithms and nomogram were comparable on the testing datasets. However, the nomogram is easier to apply and therefore more useful to aid surgical decision making in clinical practice.CONCLUSIONSComorbidity status and BMI are newly identified factors associated with decreased survival and should be taken into account when estimating survival. Performance of the boosting algorithms and nomogram were comparable on the testing datasets. However, the nomogram is easier to apply and therefore more useful to aid surgical decision making in clinical practice.Level III, prognostic study.LEVEL OF EVIDENCELevel III, prognostic study. Survival estimation guides surgical decision-making in metastatic bone disease. Traditionally, classic scoring systems, such as the Bauer score, provide survival estimates based on a summary score of prognostic factors. Identification of new factors might improve the accuracy of these models. Additionally, the use of different algorithms--nomograms or boosting algorithms--could further improve accuracy of prognostication relative to classic scoring systems. A nomogram is an extension of a classic scoring system and generates a more-individualized survival probability based on a patient's set of characteristics using a figure. Boosting is a method that automatically trains to classify outcomes by applying classifiers (variables) in a sequential way and subsequently combines them. A boosting algorithm provides survival probabilities based on every possible combination of variables. We wished to (1) assess factors independently associated with decreased survival in patients with metastatic long bone fractures and (2) compare the accuracy of a classic scoring system, nomogram, and boosting algorithms in predicting 30-, 90-, and 365-day survival. We included all 927 patients in our retrospective study who underwent surgery for a metastatic long bone fracture at two institutions between January 1999 and December 2013. We included only the first procedure if patients underwent multiple surgical procedures or had more than one fracture. Median followup was 8 months (interquartile range, 3-25 months); 369 of 412 (90%) patients who where alive at 1 year were still in followup. Multivariable Cox regression analysis was used to identify clinical and laboratory factors independently associated with decreased survival. We created a classic scoring system, nomogram, and boosting algorithms based on identified variables. Accuracy of the algorithms was assessed using area under the curve analysis through fivefold cross validation. The following factors were associated with a decreased likelihood of survival after surgical treatment of a metastatic long bone fracture, after controlling for relevant confounding variables: older age (hazard ratio [HR], 1.0; 95% CI, 1.0-1.0; p < 0.001), additional comorbidity (HR, 1.2; 95% CI, 1.0-1.4; p = 0.034), BMI less than 18.5 kg/m(2) (HR, 2.0; 95% CI, 1.2-3.5; p = 0.011), tumor type with poor prognosis (HR, 1.8; 95% CI, 1.6-2.2; p < 0.001), multiple bone metastases (HR, 1.3; 95% CI, 1.1-1.6; p = 0.008), visceral metastases (HR, 1.6; 95% CI, 1.4-1.9; p < 0.001), and lower hemoglobin level (HR, 0.91; 95% CI, 0.87-0.96; p < 0.001). The survival estimates by the nomogram were moderately accurate for predicting 30-day (area under the curve [AUC], 0.72), 90-day (AUC, 0.75), and 365-day (AUC, 0.73) survival and remained stable after correcting for optimism through fivefold cross validation. Boosting algorithms were better predictors of survival on the training datasets, but decreased to a performance level comparable to the nomogram when applied on testing datasets for 30-day (AUC, 0.69), 90-day (AUC, 0.75), and 365-day (AUC, 0.72) survival prediction. Performance of the classic scoring system was lowest for all prediction periods. Comorbidity status and BMI are newly identified factors associated with decreased survival and should be taken into account when estimating survival. Performance of the boosting algorithms and nomogram were comparable on the testing datasets. However, the nomogram is easier to apply and therefore more useful to aid surgical decision making in clinical practice. Level III, prognostic study. Background Survival estimation guides surgical decision-making in metastatic bone disease. Traditionally, classic scoring systems, such as the Bauer score, provide survival estimates based on a summary score of prognostic factors. Identification of new factors might improve the accuracy of these models. Additionally, the use of different algorithms--nomograms or boosting algorithms--could further improve accuracy of prognostication relative to classic scoring systems. A nomogram is an extension of a classic scoring system and generates a more-individualized survival probability based on a patient's set of characteristics using a figure. Boosting is a method that automatically trains to classify outcomes by applying classifiers (variables) in a sequential way and subsequently combines them. A boosting algorithm provides survival probabilities based on every possible combination of variables. Questions/purposes We wished to (1) assess factors independently associated with decreased survival in patients with metastatic long bone fractures and (2) compare the accuracy of a classic scoring system, nomogram, and boosting algorithms in predicting 30-, 90-, and 365-day survival. Methods We included all 927 patients in our retrospective study who underwent surgery for a metastatic long bone fracture at two institutions between January 1999 and December 2013. We included only the first procedure if patients underwent multiple surgical procedures or had more than one fracture. Median followup was 8 months (interquartile range, 3-25 months); 369 of 412 (90%) patients who where alive at 1 year were still in followup. Multivariable Cox regression analysis was used to identify clinical and laboratory factors independently associated with decreased survival. We created a classic scoring system, nomogram, and boosting algorithms based on identified variables. Accuracy of the algorithms was assessed using area under the curve analysis through fivefold cross validation. Results The following factors were associated with a decreased likelihood of survival after surgical treatment of a metastatic long bone fracture, after controlling for relevant confounding variables: older age (hazard ratio [HR], 1.0; 95% CI, 1.0-1.0; p < 0.001), additional comorbidity (HR, 1.2; 95% CI, 1.0-1.4; p = 0.034), BMI less than 18.5 kg/m^sup 2^ (HR, 2.0; 95% CI, 1.2-3.5; p = 0.011), tumor type with poor prognosis (HR, 1.8; 95% CI, 1.6-2.2; p < 0.001), multiple bone metastases (HR, 1.3; 95% CI, 1.1-1.6; p = 0.008), visceral metastases (HR, 1.6; 95% CI, 1.4-1.9; p < 0.001), and lower hemoglobin level (HR, 0.91; 95% CI, 0.87-0.96; p < 0.001). The survival estimates by the nomogram were moderately accurate for predicting 30-day (area under the curve [AUC], 0.72), 90-day (AUC, 0.75), and 365-day (AUC, 0.73) survival and remained stable after correcting for optimism through fivefold cross validation. Boosting algorithms were better predictors of survival on the training datasets, but decreased to a performance level comparable to the nomogram when applied on testing datasets for 30-day (AUC, 0.69), 90-day (AUC, 0.75), and 365-day (AUC, 0.72) survival prediction. Performance of the classic scoring system was lowest for all prediction periods. Conclusions Comorbidity status and BMI are newly identified factors associated with decreased survival and should be taken into account when estimating survival. Performance of the boosting algorithms and nomogram were comparable on the testing datasets. However, the nomogram is easier to apply and therefore more useful to aid surgical decision making in clinical practice. Level of Evidence Level III, prognostic study. |
| Author | Ready, John E. Hornicek, Francis J. van Dijke, Maarten Schwab, Joseph H. Raskin, Kevin A. Ferrone, Marco L. Janssen, Stein J. van der Heijden, Andrea S. |
| Author_xml | – sequence: 1 givenname: Stein J. orcidid: 0000-0003-3939-7765 surname: Janssen fullname: Janssen, Stein J. email: steinjanssen@gmail.com organization: Department of Orthopaedic Surgery, Orthopaedic Oncology Service, Massachusetts General Hospital–Harvard Medical School, Massachusetts General Hospital – sequence: 2 givenname: Andrea S. surname: van der Heijden fullname: van der Heijden, Andrea S. organization: Department of Orthopaedic Surgery, Orthopaedic Oncology Service, Massachusetts General Hospital–Harvard Medical School – sequence: 3 givenname: Maarten surname: van Dijke fullname: van Dijke, Maarten organization: Department of Orthopaedic Surgery, Orthopaedic Oncology Service, Massachusetts General Hospital–Harvard Medical School – sequence: 4 givenname: John E. surname: Ready fullname: Ready, John E. organization: Department of Orthopaedic Surgery, Orthopaedic Oncology Service, Brigham and Women’s Hospital–Harvard Medical School – sequence: 5 givenname: Kevin A. surname: Raskin fullname: Raskin, Kevin A. organization: Department of Orthopaedic Surgery, Orthopaedic Oncology Service, Massachusetts General Hospital–Harvard Medical School – sequence: 6 givenname: Marco L. surname: Ferrone fullname: Ferrone, Marco L. organization: Department of Orthopaedic Surgery, Orthopaedic Oncology Service, Brigham and Women’s Hospital–Harvard Medical School – sequence: 7 givenname: Francis J. surname: Hornicek fullname: Hornicek, Francis J. organization: Department of Orthopaedic Surgery, Orthopaedic Oncology Service, Massachusetts General Hospital–Harvard Medical School – sequence: 8 givenname: Joseph H. surname: Schwab fullname: Schwab, Joseph H. organization: Department of Orthopaedic Surgery, Orthopaedic Oncology Service, Massachusetts General Hospital–Harvard Medical School |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26155769$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | The Association of Bone and Joint Surgeons® 2015 The Association of Bone and Joint Surgeons 2015 |
| Copyright_xml | – notice: The Association of Bone and Joint Surgeons® 2015 – notice: The Association of Bone and Joint Surgeons 2015 |
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| DOI | 10.1007/s11999-015-4446-z |
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| References_xml | – reference: BryantDHaveyTCRobertsRGuyattGHow many patients? How many limbs? Analysis of patients or limbs in the orthopaedic literature: a systematic reviewThe J Bone Joint Surg Am.200688414510.2106/JBJS.E.0027216391248 – reference: IasonosASchragDRajGVPanageasKSHow to build and interpret a nomogram for cancer prognosisJ Clin Oncol.2008261364137010.1200/JCO.2007.12.979118323559 – reference: HendersonRKeidingNIndividual survival time prediction using statistical modelsJ Med Ethics.20053170370617340731:STN:280:DC%2BD2MnjvVertg%3D%3D10.1136/jme.2005.01242716319233 – reference: Allison PD. Survival Analysis Using SAS®: A Practical Guide, Cary, NC: SAS® Institute; 2010. – reference: HastieTTibshiraniRFriedmanJThe Elements of Statistical Learning: Data Mining, Inference, and Prediction2009New York, NYSpringer-Verlag10.1007/978-0-387-84858-7 – reference: ClevesMAFrom the help desk: Comparing areas under receiver operating characteristic curves from two or more probit or logit modelsStata J.20022301313 – reference: PatnaikJLByersTDiguiseppiCDenbergTDDabeleaDThe influence of comorbidities on overall survival among older women diagnosed with breast cancerJ Natl Cancer Inst.201110311011111313958510.1093/jnci/djr18821719777 – reference: SogaardMThomsenRWBossenKSSorensenHTNorgaardMThe impact of comorbidity on cancer survival: a reviewClin Epidemiol.20135suppl 1329382048310.2147/CLEP.S4715024227920 – reference: QuanHLiBCourisCMFushimiKGrahamPHiderPJanuelJMSundararajanVUpdating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countriesAm J Epidemiol.201117367668210.1093/aje/kwq43321330339 – reference: MackinnonAThe use and reporting of multiple imputation in medical research: a reviewJ Intern Med.20102685865931:STN:280:DC%2BC3cbotlSntw%3D%3D10.1111/j.1365-2796.2010.02274.x20831627 – reference: HarrellFEJrLeeKLMarkDBMultivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errorsStat Med.19961536138710.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-48668867 – reference: GuptaSTranTLuoWPhungDKennedyRLBroadACampbellDKippDSinghMKhasrawMMathesonLAshleyDMVenkateshSMachine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registryBMJ Open.20144e004007396310110.1136/bmjopen-2013-00400724643167 – reference: McTiernanAIrwinMVongruenigenVWeight, physical activity, diet, and prognosis in breast and gynecologic cancersJ Clin 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Survival estimation guides surgical decision-making in metastatic bone disease. Traditionally, classic scoring systems, such as the Bauer score,... Survival estimation guides surgical decision-making in metastatic bone disease. Traditionally, classic scoring systems, such as the Bauer score, provide... Background Survival estimation guides surgical decision-making in metastatic bone disease. Traditionally, classic scoring systems, such as the Bauer score,... |
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| SubjectTerms | Algorithms Awards and Prizes Bone Neoplasms - complications Bone Neoplasms - mortality Bone Neoplasms - secondary Conservative Orthopedics Female Fractures, Bone - etiology Fractures, Bone - mortality Fractures, Spontaneous - etiology Fractures, Spontaneous - mortality Humans Male Medicine Medicine & Public Health Middle Aged Nomograms Orthopedics Prognosis Retrospective Studies Society Awards Sports Medicine Surgery Surgical Orthopedics Survival Analysis Tumor |
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| Title | 2015 Marshall Urist Young Investigator Award: Prognostication in Patients With Long Bone Metastases: Does a Boosting Algorithm Improve Survival Estimates? |
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