Epistaxis: What Do People Know and What Do They Do?
Bibliographic record
Abstract
Aim: The aim of this study is to assess the current knowledge of the first-aid management of epistaxis and misconceptions among the general Saudi population. Methods: A survey questionnaire was developed and was distributed through text message, E-mail, social networks, various websites, and web forums among the Saudi population. Responses were collected over a period of 2 months. Knowledge was assessed based on correct responses to six main questions. Five to six correct answers were considered as excellent knowledge, 3–4 as good knowledge, and 2 and below as poor knowledge. Results: There were 1760 individuals who responded to the survey, 577 (32.8%) were males. There were 828 respondents (47%) who received information on the first-aid management of epistaxis, the most common source of information was through a relative or a friend (15.7%). Only 199 respondents (11.3%) will apply pressure to control epistaxis, 99 (5.6%) knows where to correctly press, and 84 (4.78%) will correctly tilt the head forward. There were 132 respondents (7.5%) who thought that patients should be brought to the ER in all cases of epistaxis. There were 1111 respondents (63.2%) who have poor knowledge of first-aid management of epistaxis. Conclusion: There is poor knowledge of the first-aid management of epistaxis in the surveyed Saudi population. Increased awareness and information dissemination programs on the first-aid management of epistaxis can improve knowledge and recall among the general population.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".