The role of emergency department crowding on the implementation of efficiency strategies to improve care for adult patients presenting with chest pain.
Bibliographic record
Abstract
IntroductionThis study evaluated the operational and clinical outcomes for patients presenting to an emergency department (ED) with chest pain using a 2-hour high-sensitivity troponin T (hs-TnT) protocol compared to an existing 3-hour hs-TnI protocol. MethodsThis retrospective cohort study used eight population-based linked health administrative datasets for adult patients presenting to a Canadian urban ED with chest pain over a two-year period. The primary outcome was ED length of stay (LOS). Secondary outcomes included health outcomes within 30 days. ResultsOverall, 5426 patients were included; 2879 and 2547 in the hs-TnI group and hs-TnT groups, respectively. The cohorts were similar: the median age was 58 years, 55% male, and comorbidities matched. Delays in physician initial assessment (PIA; +54 min; 95% CI: 42.2-65.8) and patients leaving without being seen increased (7% vs 12.5%; P < 0.0001) due to worsening overcrowding. The median ED LOS increased from 448 to 481 minutes after hs-TnT implementation (median difference = 33.0; 95% CI: 19.9 to 46.1), although time to second tests decreased by 54 minutes. At 30 days, there were no differences in readmission or death before and after the intervention. ConclusionImplementation of a 2-hour hs-TnT protocol resulted in mixed outcomes: increased PIA and ED LOS, yet faster time to second test. The journey through the ED for patients with chest pain is complex and affected by issues beyond the assay type and the timing of repeat Tn measurements. ED overcrowding needs to be addressed for efficiency to be realized in this setting.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".