Bibliographic record
Abstract
thinking on your feetEmergency medicine (EM) was born from a public demand for a high-quality emergency service that would always be available and was meant to serve as a link between out-of-hospital services and in-hospital critical care. 1 Since the 1950s, hospitals have been the preferred locations for around-the-clock diagnostic testing and medical care.Originally, patients went to the emergency department (ED) after hours, when they could not access health care anywhere else. 1 Today, EDs are notoriously known as places of overcrowding, long wait-times and frustrated patients.2 This will continue to be a national problem.ED crowding leads to delays in care, increased mortality, decreased patient satisfaction and physician burn-out.This problem is an every-day reality for many EM physicians, reflects a system-wide performance issue and can be an indicator of health care quality.3 The causes of ED overcrowding are multifactorial.Multiple strategies have been implemented and evaluated in an effort to improve this situation: new technologies, triage systems and care-accelerating interventions.In the following section, we have outlined and evaluated four strategies to reduce ED overcrowding.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".