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Record W2956430800 · doi:10.4103/jnsm.jnsm_10_18

Epistaxis: What Do People Know and What Do They Do?

2018· article· en· W2956430800 on OpenAlexaff
Ahmed H Saleem, Abdullah M Alahwal, Ahmed Alsayed, Manal Bin-Manie, Hani Marzouki

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldMedicine
TopicVascular Anomalies and Treatments
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNeed to knowPsychologyInternet privacyHistoryComputer scienceComputer security

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0060.006
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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.

Opus teacher head0.119
GPT teacher head0.502
Teacher spread0.383 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

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