Are spine metastasis survival scoring systems outdated and do they underestimate life expectancy? Caution in surgical recommendation guidance
Bibliographic record
Abstract
OBJECTIVE: Survival scoring systems for spine metastasis (SPM) were designed to help surgical practice. The authors sought to validate the prognostic accuracy of the main preoperative scoring systems for SPM. METHODS: It was hypothesized that true patient survival in SPM was better than that predicted using prognosis scores. To investigate this hypothesis, the authors designed a French national retrospective study of a prospectively collected multicenter database involving 739 patients treated for SPM between 2014 and 2017. RESULTS: In this series, the median survival time for all patients from an SPM diagnosis was 17.03 ± 1.5 months. Sensitivity and specificity were estimated using the area under the curve (AUC). The AUC of Tomita's prognosis score was the lowest and poorest (0.4 ± 0.023, range 0.35-0.44), whereas the AUC of the Tokuhashi score was the highest (0.825). The Lei score presented an AUC of 0.686 ± 0.022 (range 0.64-0.7), and the Rades score showed a weaker AUC (0.583 ± 0.020, range 0.54-0.63). Differences among AUCs were all statistically significant (p < 0.001). The modified Bauer score and the Rades score had the highest rate of agreement in predicting survival, with a weighted Cohen's kappa of 0.54 and 0.41, respectively, indicating a moderate agreement. The revised Tokuhashi and Lei scores had a fair rate of agreement (weighted Cohen's kappa = 0.24 and 0.22, respectively). The van der Linden and Tomita scores demonstrated the worst performance, with only a "slight" rate of agreement (weighted Cohen's kappa = 0.19 and 0.16, respectively) between what was predicted and the actual survival. CONCLUSIONS: The use of prognostic scoring systems in the estimation of survival in patients with SPM has become obsolete and therefore underestimates survival. Surgical treatment decisions should no longer be based on survival estimations alone but must also take into account patient symptoms, spinal instability, and quality of life.
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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 | 0.130 | 0.379 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".