ORIGINAL CONTRIBUTION Overcrowding in U.S. EDS: A Critical Condition
Bibliographic record
Abstract
The issue of overcrowding in Emergency Departments (EDs) has become a significant national problem. 1-5 Several recent scientific studies have added documentation of the overcrowding problem. 6-8 In addition, discussions with emergency physicians across the country and at national meetings indicate that this is a major problem. 9-11 Many private, academic, and urban EDs in both large and small communities are frequently subjecting patients to long delays compared with many years ago, when such overcrowding occurred primarily in inner-city EDs on Friday or Saturday nights. 1 Recently, the issue of ED overcrowding was brought before the state legislator and other California agencies. 12-14 The United States is not alone in the overcrowding problem. In Canada, the issue of overcrowding in “accident and emergency rooms ” is a serious national issue. 15 In Australia, ED overcrowding in Sydney has resulted in ambulance diversions from hospitals. Other countries, including
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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 | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.005 |
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".