Standardized Reporting of Workflow Metrics in Acute Ischemic Stroke Treatment: Why and How?
Bibliographic record
Abstract
The benefit of acute ischemic stroke (AIS) treatment is highly time dependent. Although studies on workflow improvement in AIS are increasingly gaining attention, there is a lack of consensus and consistency regarding the definition, measurement, and reporting of AIS workflow times. We discuss the challenges related to defining and measuring workflow times in AIS and propose a basic set of time intervals that should be reported in AIS workflow studies. We particularly focus on patients undergoing mechanical thrombectomy. Importantly, endovascular treatment workflow times should always be reported in conjunction with reperfusion quality because one is not informative without the other. We further suggest standardized reporting of workflow times that includes the 90th percentile in addition to medians and interquartile ranges, means, and SDs. The proposed methodology serves as a framework for AIS studies and aids further discussion on workflow-related AIS research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".