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Record W2817503133 · doi:10.1002/cncr.31585

Predictive factors of survival in a surgical series of metastatic epidural spinal cord compression and complete external validation of 8 multivariate models of survival in a prospective North American multicenter study

2018· article· en· W2817503133 on OpenAlexaff
Anick Nater, Lindsay Tetreault, Branko Kopjar, Paul M. Arnold, Mark B. Dekutoski, Joel Finkelstein, Charles G. Fisher, John C. France, Ziya L. Gokaslan, Laurence D. Rhines, Peter S. Rose, Arjun Sahgal, James M. Schuster, Alexander R. Vaccaro, Michael G. Fehlings

Bibliographic record

VenueCancer · 2018
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsUniversity of British ColumbiaVancouver Coastal HealthSunnybrook Health Science CentreToronto Western HospitalUniversity of TorontoUniversity Health Network
FundersNational Institutes of Health
KeywordsMedicineHazard ratioProportional hazards modelUnivariate analysisSpinal cord compressionMultivariate analysisInternal medicineProspective cohort studyUnivariateSurgeryOncologyMultivariate statisticsConfidence intervalSpinal cord

Abstract

fetched live from OpenAlex

BACKGROUND: This study was designed to identify preoperative predictors of survival in surgically treated patients with metastatic epidural spinal cord compression (MESCC), to examine how these predictors are related to 8 prognostic models, and to perform the first full external validation of these models in accordance with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. METHODS: One hundred forty-two surgically treated patients with MESCC were enrolled in a prospective, multicenter North American cohort study and were followed for 12 months or until death. Cox regression was used. Noncollinear predictors with < 10% missing data, with ≥ 10 events per stratum, and with P < .05 in a univariate analysis were tested through a backward stepwise selection. For the original and revised Tokuhashi prognostic scoring systems (PSSs), Tomita PSS, modified Bauer PSS, van der Linden PSS, Bartels model, Oswestry Spinal Risk Index, and Bollen PSS, this study examined calibration graphically, discrimination with Harrell c-statistics, and survival stratified by risk groups with the Kaplan-Meier method and log-rank test. RESULTS: The following were significant in the univariate analysis: type of primary tumor, sex, organ metastasis, body mass index, preoperative radiotherapy to MESCC, physical component (PC) of the 36-Item Short Form Health Survey, version 2 (SF-36v2), and EuroQol 5-Dimension (EQ-5D) Questionnaire. Breast, prostate and thyroid primary tumor (HR: 2.9; P =.0005), presence of organ metastasis (hazard ratio (HR): 2.0; P = .005) and SF-36v2 PC (HR: 0.95; P < .0001) were associated with survival in multivariable analysis. Predicted prognoses poorly matched observed values on calibration plots; Bartels model calibration slope was 0.45. Bollen PSS (0.61; 95% CI: 0.58-0.64) and Bartels model (0.68; 95% CI: 0.65-0.71) had the lowest and highest c-statistics, respectively. CONCLUSIONS: The primary tumor type (breast, prostate, or thyroid), an absence of organ metastasis, and a lower degree of physical disability are preoperative predictors of longer survival for surgical MESCC patients. These results are in keeping with current models. This full external validation of 8 prognostic PSSs or model of survival in surgical MESCC patients has revealed that calibration is poor, especially for long-term survivors, whereas discrimination is possibly helpful.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.362
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations50
Published2018
Admission routes1
Has abstractyes

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