The effect of a GP’s perception of a patient request for antibiotics on antibiotic prescribing for respiratory tract infections: secondary analysis of a point-prevalence audit survey in 18 European countries
Bibliographic record
Abstract
BACKGROUND: Illness severity, comorbidity, fever, age, and symptom duration influence antibiotic prescribing for respiratory tract infections (RTI). Non-medical determinants, such as patient expectations, also impact prescribing. AIM: To quantify the effect of a GP's perception of a patient request for antibiotics on antibiotic prescribing for RTI and investigate effect modification by medical determinants and country. DESIGN & SETTING: Prospective audit of general practices in 18 European countries. METHOD: Consultation data were registered of 4982 patients presenting with acute cough and/or sore throat. A mixed-effect logistic regression model analysed the effect of GPs' perceptions of a patient request for antibiotics. Two-way interaction terms assessed effect modification. Relevant clinical findings were added to subgroups of lower RTI (LRTI), throat infection, and influenza-like-illness (ILI). RESULTS: A GP's perception of a request for antibiotics meant they were four times more likely to prescribe antibiotics (odds ratio [OR] 4.4, 95% confidence interval [CI] = 3.4 to 5.5). This effect varied by country: lower in Spain (OR 0.06), Ukraine (OR 0.15), and Greece (OR 0.22) compared with the lowest prescribing country. The effect was higher for ILI (OR 13.86, 95% CI = 5.5 to 35) and throat infection (OR 5.1, 95% CI = 3.1 to 8.4) than for LRTI (OR 2.9, 95% CI = 1.9 to 4.3). For ILI and LRTI, GPs were more likely to prescribe antibiotics with abnormal lung auscultation and/or increased or purulent sputum and for throat infection, with tonsillar exudate and/or swollen tonsils. CONCLUSION: GPs' perceptions of an antibiotic request and specific clinical findings influence antibiotic prescribing. Incorporating exploration of patient expectations, point-of-care testing, and discussing watchful waiting into the decision-making process will benefit appropriate prescribing of antibiotics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".