Timely Reperfusion in Stroke and Myocardial Infarction Is Not Correlated
Bibliographic record
Abstract
Background— Timely reperfusion is critical in acute ischemic stroke (AIS) and ST-segment–elevation myocardial infarction (STEMI). The degree to which hospital performance is correlated on emergent STEMI and AIS care is unknown. Primary objective of this study was to determine whether there was a positive correlation between hospital performance on door-to-balloon (D2B) time for STEMI and door-to-needle (DTN) time for AIS, with and without controlling for patient and hospital differences. Methods and Results— Prospective study of all hospitals in both Get With The Guidelines-Stroke and Get With The Guidelines-Coronary Artery Disease from 2006 to 2009 and treating ≥10 patients. We compared hospital-level DTN time and D2B time using Spearman rank correlation coefficients and hierarchical linear regression modeling. There were 43 hospitals with 1976 AIS and 59 823 STEMI patients. Hospitals’ DTN times for AIS did not correlate with D2B times for STEMI (ρ=−0.09; P =0.55). There was no correlation between hospitals’ proportion of eligible patients treated within target time windows for AIS and STEMI (median DTN time <60 minutes: 21% [interquartile range, 11–30]; median D2B time <90 minutes: 68% [interquartile range, 62–79]; ρ=−0.14; P =0.36). The lack of correlation between hospitals’ DTN and D2B times persisted after risk adjustment. We also correlated hospitals’ DTN time and D2B time data from 2013 to 2014 using Get With The Guidelines (DTN time) and Hospital Compare (D2B time). From 2013 to 2014, hospitals’ DTN time performance in Get With The Guidelines was not correlated with D2B time performance in Hospital Compare (n=546 hospitals). Conclusions— We found no correlation between hospitals’ observed or risk-adjusted DTN and D2B times. Opportunities exist to improve hospitals’ performance of time-critical care processes for AIS and STEMI in a coordinated approach.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".