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Record W3127316611 · doi:10.1186/s40463-020-00485-8

Epistaxis first-aid management: A needs assessment among healthcare providers

2021· article· en· W3127316611 on OpenAlexaffabout
Leigh J. Sowerby, C Rajakumar, Matthew Davis, Brian Rotenberg

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicVascular Anomalies and Treatments
Canadian institutionsUniversity of British ColumbiaSt Joseph's Health CareWestern University
Fundersnot available
KeywordsMedicineFamily medicineEmergency departmentContinuing medical educationHealth careMedical emergencyFirst aidContinuing educationNursingMedical education

Abstract

fetched live from OpenAlex

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

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

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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations26
Published2021
Admission routes2
Has abstractyes

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