Abstract TMP9: Hospitals' Performance on Timely Reperfusion in Stroke and Myocardial Infarction is not Correlated
Bibliographic record
Abstract
Introduction: Timely reperfusion is performed less optimally in acute ischemic stroke (AIS) than in acute myocardial infarction (AMI). The degree to which hospital performance is correlated on emergent AMI and AIS care is unknown. Hypothesis: There would be a positive correlation between hospital performance on door-to-balloon time (D2B) for AMI and door-to-needle time (DTN) for AIS; and hospital performance on D2B would predict DTN even after controlling for patient and hospital differences. Methods: Prospective study of all hospitals participating in both Get With The Guidelines (GWTG)-Stroke and -Coronary Artery Disease from 2006-09 and treating ≥10 patients. We compared hospital-level DTN and D2B before and after risk adjustment using Spearman’s rank correlation coefficients and hierarchical linear regression modeling. We also correlated hospitals’ DTN and D2B data from 2013-14 using GWTG (DTN) and Hospital Compare (D2B). Results: There were 43 hospitals contributing data (1976 AIS and 59,823 AMI patients). Hospitals’ DTN times for AIS did not correlate with their D2B times for AMI (median DTN 85 min [IQR 77-99] vs. median D2B 72 min [IQR 62-81]; ρ=-0.09; p=0.55). There was no correlation between hospitals’ proportion of eligible patients treated within target time windows for AIS and AMI (median DTN<60 minutes: 21% [IQR 11-30]; median D2B<90 minutes: 68% [IQR 62-79]; ρ=-0.14; p=0.36). The lack of correlation between hospitals’ DTN and D2B times persisted after risk adjustment. From 2013-14, hospitals’ DTN performance in GWTG was not correlated with D2B performance in Hospital Compare (N=546 hospitals; see figure). 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 AMI in a coordinated rather than condition-specific manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".