A population-based study of different antibiotic prescribing in different areas.
Bibliographic record
Abstract
BACKGROUND: Respiratory tract infections are the most common reason for antibiotic prescription in Sweden as in other countries. The prescription rates vary markedly in different countries, counties and municipalities. The reasons for these variations in prescription rate are not obvious. AIM: To find possible explanations for different antibiotic prescription rates in children. DESIGN OF STUDY: Prospective population based study. SETTING: All child health clinics in four municipalities in Sweden which, according to official statistics, had high antibiotic prescription rates, and all child health clinics in three municipalities which had low antibiotic prescription rates. METHOD: During one month, parents recorded all infectious symptoms, physician consultations and antibiotic treatments, from 848 18-month-old children in a log book. The parents also answered a questionnaire about socioeconomic factors and concern about infectious diseases. RESULTS: Antibiotics were prescribed to 11.6% of the children in the high prescription area and 4.7% in the low prescription area during the study month (crude odds ratio [OR] = 2.67; 95% confidence interval [CI] = 1.45 to 4.93). After multiple logistic regression analyses taking account of socioeconomic factors, concern about infectious illness, number of symptom days and physician consultations, differences in antibiotic prescription rates remained (adjusted OR = 2.61; 95% CI = 1.14 to 5.98). The variable that impacted most on antibiotic prescription rates, although it was not relevant to the geographical differences, was a high level of concern about infectious illness in the family. CONCLUSIONS: The differences in antibiotic prescription rates could not be explained by socioeconomic factors, concern about infectious illness, number of symptom days and physician consultations. The differences may be attributable to different prescription behaviour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".