Use of the "crash room" in a rural hospital: case review of 100 cases.
Bibliographic record
Abstract
INTRODUCTION: There is little published literature about the characteristics of patients with high triage levels seen in the emergency departments of rural hospitals. We sought to determine the demographics of patients brought into the "crash room" of a rural hospital, to assess the pathologies that brought them to the hospital and to study their final disposition. METHODS: We conducted a retrospective chart review of visits to the crash room of our rural hospital. We used the hospital's crash room register to compile a list of the last 100 consecutive visits to the crash room as of July 20, 2011. We extracted initial data from the register and additional data by chart review. RESULTS: Patients with triage levels 1 to 3 were brought to the crash room at a rate of 0.36 cases/wk/1000 population. Although circulatory disease, respiratory disease and "chest pain" accounted for 44.6% of final diagnoses, a wide range of pathology was seen in the crash room. Trauma and poisonings, and mental disorders accounted for 21.0% and 9.0% of diagnoses, respectively. The final diagnosis was nonspecific, vague or "unknown" in 20% of the visits. Of the crash room cases, 17% required transfer to a secondary care hospital. CONCLUSION: Crash room visits in this rural hospital occurred at a rate of 0.48 cases/wk/1000 population. Most patients seen in the crash room were not given the traditional triage levels 1 or 2 that are usually associated with crash room care. The final diagnosis was nonspecific in 17.0% of cases, and mental disorders accounted for 9.0% of crash room visits.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.004 | 0.001 |
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".