C.02 Predictors of survival in a surgical series of Metastatic Spinal Cord Compression and a complete external validation of 8 models in a prospective multi-centre study
Bibliographic record
Abstract
Background: We aimed to identify preoperative predictors of survival in Metastatic Epidural Spinal Cord Compression (MESCC) patients surgically treated, examine how these predictors relate to eight prognostic models, and to perform the first full external validation of these models in accordance with the TRIPOD statement. Methods: 142 surgically treated MESCC patients were enrolled in a prospective, multicenter cohort study and followed for 12 months or until death. Cox proportional hazards (PH) regressions were used. Non-collinear predictors with <10% missing data, ≥10 events per stratum and p<0.05 in univariable analysis were tested through a backward stepwise selection. For the original and revised Tokuhashi, Tomita, modified Bauer, van der Linden, Bartels, OSRI, Bartels and Bollen, we examined calibration and discrimination; survival stratified by risk groups with the Kaplan-Meier method and log-rank test. Results: Primary tumor, organ metastasis and SF-36v2 PC were associated with survival in multivariable analysis; corrected discrimination was 0.68. These three predictors were common to most current prognostic models. However, calibration was poor overall while discrimation was possibly helpful. Conclusions: Primary tumor type (breast, prostate and thyroid), absence of organ metastasis, and a lower degree of physical disability are preoperative predictors of longer survival in surgical MESCC patients. Clinicians should use these 8 prognostic models with caution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".