MétaCan
Menu
Back to cohort

Treatment of acute otitis media in patients with a reported penicillin allergy

2000· article· en· W2030384513 on OpenAlexaff
Falconer, David M. Gardner

Bibliographic record

VenueJournal of Clinical Pharmacy and Therapeutics · 2000
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineAmoxicillinTolerabilityPenicillinOtitisAntibioticsAllergyRashTrimethoprimDermatologyIntensive care medicineAdverse effectPediatricsInternal medicineSurgeryImmunologyMicrobiology

Abstract

fetched live from OpenAlex

Otitis media occurs commonly in children, and is usually treated with an antibiotic. In this case report, amoxicillin was prescribed for a 6-year-old boy suffering from acute otitis media. As he had previously experienced a rash after the administration of a penicillin, the medication order was switched from amoxicillin to trimethoprim/sulfamethoxazole (TMP/SMX). In an effort to determine whether or not this intervention was appropriate, references were found using Medline, International Pharmaceutical Abstracts and the Cochrane Library. Issues to be addressed included the need for antibiotics in acute otitis media, the comparative efficacy and tolerability of antimicrobial agents and the reliability of reported penicillin allergies. Amoxicillin and TMP/SMX were found to be first-line agents in the treatment of acute otitis media owing to their efficacy, safety and cost, with neither drug being significantly better than the other. The need to treat otitis media with antibiotics remains controversial. Reported penicillin allergies were found to be an unreliable indicator of a potentially serious reaction. In conclusion, it was found that treatment with TMP/SMX was an appropriate intervention.

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)0.001
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.400
Teacher spread0.316 · 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 designNot applicable
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

Citations1
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Pharmacy and TherapeuticsSame topicEar Surgery and Otitis MediaFrench-language works237,207