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Record W3167150367 · doi:10.46747/cfp.6706427

Recurrent epistaxis in children

2021· article· en· W3167150367 on OpenAlexvenueno aff
Tyler D. Yan, Ran D. Goldman

Bibliographic record

VenueCanadian Family Physician · 2021
Typearticle
Languageen
FieldMedicine
TopicVascular Anomalies and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineToddlerPsychological interventionPediatricsIntervention (counseling)PsychiatryPsychology

Abstract

fetched live from OpenAlex

QUESTION: A child came to my clinic complaining of recurrent epistaxis with several episodes occurring every year since he was a toddler. The nosebleeds affect both nostrils, often lasting for an extended period of time and occurring in no apparent seasonal pattern. What interventions are safe and effective for recurrent epistaxis in children, and which patients warrant hematologic testing? ANSWER: Epistaxis affects more than half of children by the time they are 10 years old, with 9% of children reported to have recurrent episodes. Most cases are of benign origin and will not require further workup. For those seeking intervention, nasal mucosal hydration, such as emollient application, or humidification resolves up to 65% of cases, and many novel interventions have shown promise in their respective initial studies. Standardized bleeding questionnaires have demonstrated usefulness in decision making for further coagulation studies, taking into account historical features including frequency, duration, bleeding site, seasonal correlation, and severity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.011
GPT teacher head0.229
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2021
Admission routes1
Has abstractyes

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