Prediction Accuracy of Common Prognostic Scoring Systems for Metastatic Spine Disease
Bibliographic record
Abstract
STUDY DESIGN: A prospective multicenter cohort study. OBJECTIVE: To assess the clinical accuracy of six commonly cited prognostic scoring systems for patients with spinal metastases. SUMMARY OF BACKGROUND DATA: There are presently several available methods for the estimation of prognosis in metastatic spinal disease, but none are universally accepted by surgeons for clinical use. These scoring systems have not been rigorously tested and validated in large datasets to see if they are reliable enough to inform day-to-day patient management decisions. We tested these scoring systems in a large cohort of patients. A total of 1469 patients were recruited into a secure internet database, and prospectively collected data were analyzed to assess the accuracy of published prognostic scoring systems. METHODS: We assessed six prognostic scoring systems, described by the first authors Tomita, Tokuhashi, Bauer, van der Linden, Rades, and Bollen. Kaplan-Meier survival estimates were created for different patient subgroups as described in the original publications. Harrell's C-statistic was calculated for the survival estimates, to assess the concordance between estimated and actual survival. RESULTS: All the prognostic scoring systems tested were able to categorize patients into separate prognostic groups with different overall survivals. However none of the scores were able to achieve "good concordance" as assessed by Harrell's C-statistic. The score of Bollen and colleagues was found to be the most accurate, with a Harrell's C-statistic of 0.66. CONCLUSION: No prognostic scoring system was found to have a good predictive value. The scores of Bollen and Tomita were the most effective with Harrell's C-statistic of 0.66 and 0.65, respectively. Prognostic scoring systems are calculated using data from previous years, and are subject to inaccuracies as treatments advance in the interim. We suggest that other methods of assessing prognosis should be explored, such as prognostic risk calculation. LEVEL OF EVIDENCE: 3.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".