急性缺血性卒中血管内治疗质量改进的多学会共识
Bibliographic record
Abstract
目的这份国际性多学科共识文件将对急性缺血性卒中诊治过程和临床转归的质量标准进行定义,并希望在质量保证程序中使用这些标准,从而评估和改善急性卒中血运重建的过程和转归。材料和方法写作组成员由美国神经放射学学会、加拿大介入放射学协会、欧洲心血管和介入放射学学会、心血管造影和介入学会、介入放射学学会、神经介入外科学会、欧洲微创神经病学治疗学会以及血管和介入神经病学学会认命。写作组回顾了1986年至2012年2月期间的相关文献,对急性缺血性卒中的诊疗过程和转归进行总结形成一份证据表,然后通过共识设立性能指标和阈值。本指南得到发起学会的批准,并计划在3年后进行全面更新。结果这份国际性多学科共识文件对诊治过程和临床转归的质量标准进行了定义,包括从入院到进行影像学检查、动脉穿刺和血运重建的时间间隔以及90d时的临床转归评估标准。结论本文件为急性缺血性卒中血管内血运重建程序提供了质量改进指南。
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".