P.145 RANO-BM response criteria verification study in a SRS-treated cohort
Bibliographic record
Abstract
Background: Brain metastases are frequently seen in neurosurgical practice. Standardised criteria are created to better classify these common pathologies in research studies. This study’s goal was to evaluate RANO-BM criteria’s current thresholds in a cohort of patients with brain metastases managed by SRS. Methods: We performed a retrospective metastasis-level analysis of patients treated with SRS for brain metastases. The data collected included cohort demographics, metastases characteristics, outcomes, and the rate of true positives, false negatives, true negatives and false positives as defined by RANO-BM criteria at last follow-up before second SRS. Results: 251 metastases in 50 patients were included in the analysis. RANO-BM criteria using current thresholds yielded a sensitivity of 38%, a specificity of 95%, a positive predictive value of 71% and a negative predictive value of 84%. Modified RANO-BM criteria using absolute diameter differences of 2.5 mm yielded a sensitivity of 83%, a specificity of 87%, a positive predictive value of 67% and a negative predictive value of 94%. Pseudoprogression occurred significantly earlier than tumor progression, with a median time of onset of 6.9 months and 12.1 months respectively. Conclusions: Current RANO-BM criteria unreliably identifies clinically relevant tumor progression, but are useful in assessing diameter increases caused by tumor progression and pseudoprogression.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".