P.051 Validation of the unruptured intracranial aneurysm treatment score against “real-world” MDT decisions
Bibliographic record
Abstract
Background: Intracranial aneurysms are relatively common and often incidentally detected. Elective treatment may eliminate the risk of future hemorrhage, but carries risks of permanent deficit or death. Case-control studies have suggested factors predisposing to aneurysm rupture as well as risks of elective aneurysm repair. A clinical tool was recently developed to weigh benefits of repair against treatment risks. We evaluate its performance against real-world clinical decisions made by a cerebrovascular multidisciplinary team (MDT). Methods: Chart review of all patients with unruptured intracranial berry aneurysms (UIA) discussed at cerebrovascular MDT rounds 2008-2015. Management decisions and clinical outcomes were recorded. The Unruptured Intracranial Aneurysm Treatment Score (UIATS) was calculated for each patient (each aneurysm in the case of multiple UIA). Results: We identified 240 patients with a total of 279 aneurysms. UIATS recommended aneurysm repair in 79 cases, conservative management in 88 cases, and was equivocal in 112 cases. Where the UIATS gave a clear decision, that decision was concordant with the MDT decision in 119/167 cases (71%). Discordant decisions often related to the presence of comorbidities. Clinical outcomes did not differ in cases where the recommendations were clearly concordant vs. discordant. Conclusions: The UIATS may provide guidance to non-expert clinicians. It did not outperform the MDT.
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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 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".