Bibliographic record
Abstract
What each tells us about overcrowding In this issue of the journal,1 Fatovich and Hirsch report on the ambulance bypass experience of one hospital in Western Australia from 1999 to 2001. Like in other jurisdictions,2–4 ambulance bypass has become more frequent reflecting worsening overcrowding in emergency departments (ED). Of particular interest are the insights of ED staff who recorded their perceptions of the main causes of overcrowding at the time an ambulance bypass was initiated. Not surprisingly, the great majority of bypass episodes resulted from excess patient volume, including too many new patients presenting in a short period of time for care, an inability to move admitted patients out of the ED to ward beds fast enough, or both. Other causes, including facility problems, staff shortages, an excess of high acuity patients, or external disasters were much less common. For physicians working in most EDs in the UK, Canada, or the USA, the fact that Australia is also experiencing worsened ED overcrowding will come as no great surprise. But if a similar problem exists in another part of the world, are the important causes necessarily the same as those close to home? In other words, can we assume that the Australian experience is directly relevant to our own? ED overcrowding strikes hospital systems, not patients, thus the comparability of those systems is of paramount importance when comparing the problem in different settings. Unlike illnesses such as acute myocardial infarction where predictors like age, sex, or diabetes have straightforward definitions, the predictors of ED overcrowding are largely logistical in origin. The usual causes include patient volume, …
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".