Uncertainty and Agreement Regarding the Role of Flow Diversion in the Management of Difficult Aneurysms
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The role of flow diversion in the management of aneurysms remains unknown. We sought to evaluate the community agreement regarding indications for flow diversion. MATERIALS AND METHODS: A portfolio of 35 difficult aneurysm cases was sent to 40 clinicians with varying backgrounds and experience. Responders were asked whether they considered flow diversion a treatment option, whether other options were possible, whether recruitment in a randomized trial would be considered, and to select their final choice. Agreement was studied by using κ statistics. RESULTS: Decisions for flow diversion were more frequent (n = 300, 39%) than decisions to coil (n = 163, 21.2%), to observe (n = 121, 15.7%), to occlude the parent vessel (n = 102, 13.2%), or to clip (n = 66, 8.6%). Sidewall aneurysm morphology was associated with flow diversion as the final choice (P = .001). Interjudge agreement was fair at best (κ <0.3) for all cases and all judges, despite high certainty levels (range, 7.2-8.9 ± 2.0 on a 0-10 scale). Agreement was no better within specialties or with more experience. All patients were judged to have other treatment options. Judges were willing to offer trial participation in 417 of 741 (56.3%) scenarios, more frequently when the aneurysm was sidewall (P = .001) or in the anterior circulation (P = .028). CONCLUSIONS: Individuals did not agree regarding the indications for flow diversion. There is sufficient uncertainty to justify trials designed to protect patients from the potential risks of premature adoption of an innovation.
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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 | 0.127 | 0.373 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".