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Record W2120841197 · doi:10.1503/cmaj.070151

Predictors of inappropriate antibiotic prescribing among primary care physicians

2007· article· en· W2120841197 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Medical Association Journal · 2007
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsMontreal General HospitalMcGill University
Fundersnot available
KeywordsMedicineMedical prescriptionAntibioticsConfidence intervalLogistic regressionRespiratory tract infectionsOdds ratioInternal medicineFamily medicinePediatricsIntensive care medicineRespiratory system

Abstract

fetched live from OpenAlex

BACKGROUND: Inappropriate use of antibiotics promotes antibiotic resistance. Little is known about physician characteristics that may be associated with inappropriate antibiotic prescribing. Our objective was to assess whether physician knowledge, time in practice, place of training and practice volume explain the differences in antibiotic prescribing among physicians. METHODS: A historical cohort of 852 primary care physicians in Quebec who became certified between 1990 and 1993 was followed for their first 6-9 years of practice (1990-1998). We evaluated whether inappropriate antibiotic prescribing had occurred during the study period (1990-1998) for viral (prescription of antibiotics) and bacterial (prescription of second-or third-line antibiotics given orally) infections. We used logistic regression to estimate the independent contributions of time in practice, practice volume, place of medical training and scores on licensure examinations. Physician sex and visit setting were controlled for, as were patient age, sex, education, income and geographic area of residence. RESULTS: A total of 104 230 patients who received a diagnosis of a viral infection and 65 304 who received a diagnosis of a bacterial infection were included in our study. International medical graduates were more likely than University of Montréal graduates to prescribe antibiotics for viral respiratory infections (risk ratio [RR] 1.78, 95% confidence interval [CI] 1.30-2.44). Inappropriate antibiotic prescribing increased with time in practice. Physicians with a high practice volume were more likely than those with low practice volume to prescribe antibiotics for viral respiratory infections (RR 1.27, 95% CI 1.09-1.48) and to prescribe second-and third-line antibiotics as first-line treatment (RR 1.20, 95% CI 1.06-1.37). Physician scores on licensure examinations were not predictive of inappropriate antibiotic prescribing. INTERPRETATION: International medical graduates, physicians with high-volume practices and those who were in practice longer were more likely to prescribe antibiotics inappropriately. Developing effective interventions will require increased knowledge of the mechanisms that underlie these predictors of inappropriate antibiotic prescribing.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.004
GPT teacher head0.191
Teacher spread0.187 · 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