Evaluating Performance of the Spetzler-Martin Supplemented Model in Selecting Patients With Brain Arteriovenous Malformation for Surgery
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Our recently proposed point scoring model includes the widely-used Spetzler-Martin (SM)-5 variables, along with age, unruptured presentation, and diffuse border (SM-Supp). Here we evaluate the SM-Supp model performance compared with SM-5, SM-3, and Toronto prediction models using net reclassification index, which quantifies the correct movement in risk reclassification, and validate the model in an independent data set. METHODS: Bad outcome was defined as worsening between preoperative and final postoperative modified Rankin Scale score. Point scores for each model were used as predictors in logistic regression and predictions evaluated using net reclassification index at varying thresholds (10%-30%) and any threshold (continuous net reclassification index >0). Performance was validated in an independent data set (n=117). RESULTS: Net gain in risk reclassification was better using the SM-Supp model over a range of threshold values (net reclassification index=9%-25%) and significantly improved overall predictions for outcomes in the development data set, yielding a continuous net reclassification index of 64% versus SM-5, 67% versus SM-3, and 61% versus Toronto (all P<0.001). In the validation data set, the SM-Supp model again correctly reclassified a greater proportion of patients versus SM-5 (82%), SM-3 (85%), and Toronto models (69%). CONCLUSIONS: The SM-Supp model demonstrated better discrimination and risk reclassification than several existing models and should be considered for clinical practice to estimate surgical risk in patients with brain arteriovenous malformation.
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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 | 0.008 | 0.018 |
| 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".