Epistaxis first-aid management: A needs assessment among healthcare providers
Bibliographic record
Abstract
PURPOSE: To perform a needs assessment of epistaxis first-aid measures practiced by family physicians and Emergency Department (ED) staff in London, Ontario, Canada. METHODS: Paper-based multiple-choice questionnaires were distributed to participants. Participant recruitment was conducted in two parts: 1) 28 Emergency Medicine (EM) attending physicians, 21 resident physicians training in the ED, and 26 ED nurses were surveyed while on duty in the ED; 2) 27 family physicians providing walk-in or urgent care and attending a continuing medical education (CME) event were also surveyed. Respondents were asked to identify where to apply compression to the nose and how patients should be positioned during acute epistaxis. RESULTS: Regarding where to apply compression, 19% of family physicians, 43% of EM physicians, 24% of residents, and 8% of ED nurses responded correctly. Regarding positioning, all groups responded similarly with 54-62% responding correctly. Twenty-one percent of emergency physicians, 19% of residents, 11% of family physicians, and 4% of nurses responded correctly to both questions. CONCLUSIONS: Most family physicians, EM attending physicians, ED nurses, and residents could not correctly identify basic first-aid measures for acute epistaxis. This study identifies an area where knowledge is lacking and the potential for improvement in patient management and education.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".