Surgical or Endovascular Treatment of MCA Aneurysms: An Agreement Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: MCA aneurysms are still commonly clipped surgically despite the recent development of a number of endovascular tools and techniques. We measured clinical uncertainty by studying the reliability of decisions made for patients with middle cerebral artery (MCA) aneurysms. MATERIALS AND METHODS: A portfolio of 60 MCA aneurysms was presented to surgical and endovascular specialists who were asked whether they considered surgery or endovascular treatment to be an option, whether they would consider recruitment of the patient in a randomized trial, and whether they would provide their final management recommendation. Agreement was studied using κ statistics. Intrarater reliability was assessed with the same, permuted portfolio of cases of MCA aneurysm sent to the same specialists 1 month later. RESULTS: = 844/1320; [64%] responses/22 raters), while endovascular treatment was more commonly chosen by interventional neuroradiologists (1149/1500 [76.6%] responses/25 raters). Interrater agreement was only "slight" for all cases and all judges (κ = 0.094; 95% CI, 0.068-0.130). Agreement was no better within specialties or with more experience. On delayed requestioning, 11 of 35 raters (31%) disagreed with themselves on at least 20% of cases. Surgical management and endovascular treatment were always judged to be a treatment option, for all patients. Trial participation was offered to patients 65% of the time. CONCLUSIONS: Individual clinicians did not agree regarding the best management of patients with MCA aneurysms. A randomized trial comparing endovascular with surgical management of patients with MCA aneurysms is in order.
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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 | 0.085 | 0.237 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".