“Picture to Puncture”
Bibliographic record
Abstract
BACKGROUND: Comprehensive stroke centers allow for regionalization of subspecialty stroke care. Efficacy of endovascular treatments, however, may be limited by delays in patient transfer. Our goal was to identify where these delays occurred and to assess the impact of such delays on patient outcome. METHODS AND RESULTS: This was a retrospective study evaluating patients treated with endovascular therapy from November 2010 to July 2012 at our institution. We compared patients transferred from outside hospitals with locally treated patients with respect to demographics, imaging, and treatment times. Good outcomes, as defined by 90-day modified Rankin Scale scores of 0 to 2, were analyzed by transfer status as well as time from initial computed tomography to groin puncture ("picture-to-puncture" time). A total of 193 patients were analyzed, with a mean age of 65.8 ± 14.5 years and median National Institutes of Health Stroke Scale score of 19 (interquartile range, 15-23). More than two thirds of the patients (132 [68%]) were treated from referring facilities. Outside transfers were noted to have longer picture-to-puncture times (205 minutes [interquartile range, 162-274] versus 89 minutes [interquartile range, 70-119]; P<0.001), which was attributable to the delays in transfer. This corresponded to fewer patients with favorable Alberta Stroke Program Early CT Scores on preprocedural computed tomographic imaging (Alberta Stroke Program Early CT Scores >7: 50% versus 76%; P<0.001) and significantly worse clinical outcomes (29% versus 51%; P=0.003). In a logistic regression model, picture-to-puncture times were independently associated with good outcomes (odds ratio, 0.994; 95% confidence interval, 0.990-0.999; P=0.009). CONCLUSIONS: Delays in picture-to-puncture times for interhospital transfers reduce the probability of good outcomes among treated patients. Strategies to reduce such delays herald an opportunity for hospitals to improve patient outcomes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".