Compliance with oral antibiotic regimens and associated factors in Japan: Compliance survey of multiple oral antibiotics (COSMOS)
Bibliographic record
Abstract
BACKGROUND: To provide an overall picture of oral antibiotic use in Japan, we conducted a survey of patients who had been prescribed oral antibiotics. In addition, factors potentially associated with compliance were evaluated. METHODS: General practitioners at 155 GP practices throughout Japan participated in the survey. Questionnaires were collected from 1068 subjects visiting those GP practices (676 females; median age 38 y), with a collection rate of 82.9%. RESULTS: According to this survey, the overall percentage of fully compliant subjects was 74.7%. Subgroup analyses showed that compliance tended to be higher with a shorter duration of prescription and fewer doses per day, and also with a greater ease of understanding of the explanation of treatment provided by the doctor. In multivariate analysis, age, pharyngitis, number of doses per day, duration of prescription, intention to return for follow-up, and ease of understanding the explanation of treatment given by the doctor showed a statistically significant association with compliance. CONCLUSIONS: Based on our survey results, prescribing drugs taken with a minimal number of daily doses in a shorter regimen appears to be an effective strategy for improving compliance. It appears that doctors also need to raise awareness of the importance of taking antibiotics properly by clearly explaining their purpose and significance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".