Emergency Medicine and Public Health: Stopping Emergencies Before the 9‐1‐1 Call
Bibliographic record
Abstract
ractitioners of emergency medicine (EM) are all too familiar with the challenges facing our emergency care system-challenges amply documented in a variety of governmental and nongovernmental reports.[1][2][3] Across the country, we struggle with crowded conditions that hinder us from rendering timely care to our patients, increase the risk of medical errors, and contribute to adverse outcomes.4 Inbound ambulances are diverted roughly 500,000 times per year.5 Fewer and fewer specialists are willing to take emergency department (ED) calls.6 These problems are largely the result of broad societal forces that are shaping America's health care system.The United States is unique among wealthy nations for having such a large percentage of its population without health insurance.7 For most of the past three decades, the number of uninsured has grown in good times as well as bad.8 Since the last census, about 7 million Americans have lost their jobs.Because a one-percentage-point increase in the unemployment rate boosts Medicaid and the State Children's Health Insurance Program (SCHIP) enrollment by 1 million (600,000 children and 400,000 nonelderly adults), and the number of uninsured by 1 million, the current count of uninsured is probably close to 50 million Americans-a staggering number.9
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.042 | 0.011 |
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".