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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".