MétaCan
Menu
Back to cohort
Record W2416447195

Practice variations in the management of sinusitis.

2000· article· en· W2416447195 on OpenAlexaffabout
Nathalie Trinh, Hanh H. Ngo

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsInstitut Universitaire en Santé Mentale de QuébecHôpital Fleurimont
Fundersnot available
KeywordsMedicineReferralFamily medicineSinusitisMedical prescriptionPrimary careCross-sectional studyHealth careNursingSurgeryPathology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: It has been shown that controlling inappropriate practice variations decreases cost, increases efficiency, and improves quality of health care. The objective of this study was to identify practice variations in the management of acute sinusitis in primary care practice and to explore possible influential factors. DESIGN: A cross-sectional study by mail survey was conducted. SUBJECTS: Practicing primary care physicians in the province of Quebec were selected for the study. METHODS: A questionnaire was sent to a random sample of 500 physician members of the Quebec College of Physicians. RESULTS: Three hundred and twenty-seven questionnaires were returned (total response rate = 65.4%), of which 53 were excluded. Two hundred and seventy-four completed surveys were then analyzed. Practice variations in the management of acute sinusitis were observed with respect to diagnostic indicators, the use of diagnostic tools and imagery, prescription of therapeutic agents, factors influencing the choice of antimicrobial agents, and the indications for referral to a specialist. Factors affecting such variations included age, gender, practice region (urban vs. rural), volume of practice, and university affiliation. CONCLUSIONS: This survey confirms that confusion exists among primary care physicians about the recommended management of acute sinusitis, despite the recent advent of a multitude of clinical practice guidelines. These variations highlight the need for further research to clarify these issues, as well as better methods and more specific objectives for continuing medical 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 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)0.003
metaresearch head score (Gemma)0.017
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 score0.073
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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

Citations5
Published2000
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicAntibiotic Use and ResistanceFrench-language works237,207