The Management of Persistent Distal Occlusions after Mechanical Thrombectomy and Thrombolysis: An Inter- and Intrarater Agreement Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The best management of patients with persistent distal occlusion after mechanical thrombectomy with or without IV thrombolysis remains unknown. We sought to evaluate the variability and agreement in decision-making for persistent distal occlusions. MATERIALS AND METHODS: A portfolio of 60 cases was sent to clinicians with varying backgrounds and experience. Responders were asked whether they considered conservative management or rescue therapy (stent retriever, aspiration, or intra-arterial thrombolytics) a treatment option as well as their willingness to enroll patients in a randomized trial. Agreement was assessed using κ statistics. RESULTS: < .001). Interrater agreement regarding the final management decision was "slight" (κ = 0.12; 95% CI, 0.09-0.14) and did not improve when subgroups of clinicians were studied according to background, experience, and specialty or when cases were grouped according to the level of occlusion. On delayed re-questioning, 23 of 29 respondents (79.3%) disagreed with themselves on at least 20% of cases. Respondents were willing to offer trial participation in 1295 of 1860 (69.6%) cases. CONCLUSIONS: Individuals did not agree regarding the best management of patients with persistent distal occlusion after mechanical thrombectomy and IV thrombolysis. There is sufficient uncertainty to justify a dedicated randomized trial.
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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 | 0.072 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".