Shifting bottlenecks in acute stroke treatment
Bibliographic record
Abstract
We know without doubt that ‘time is brain’. How do we know this? It is a combination of data, logic, biological plausibility, and experience. Now that endovascular treatment is the standard of care,1–5 we have an obligation to focus on process improvement to maximize patient benefit. As we go down the pathway of improving these processes, it is important to understand the idea of bottlenecks. What are bottlenecks? In any complex process, not all parts of it are flow-limiting, especially when one considers parallel processing. For instance, imagine a situation where, in a particular hospital A, all endovascular stroke cases are done under general anesthesia (GA). Also imagine that, after working hours, anesthesia is usually available within 1 h of being called. The neurointerventionist is working hard with hospital administration to ensure that the nurse and technologist can be in the laboratory within 20 min instead of the current 30 min; even if successful, this would essentially be a waste of time as the bottleneck is anesthesia availability time. In the same scenario, now imagine that they took a decision to try to perform most cases without anesthesia; in this case, the availability of the team becomes the new bottleneck. This is what is meant by shifting bottlenecks. Human behavior is such that, if one component of the overall workflow is extremely slow, there is a tendency to not worry about a few minutes here and there since the one …
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".