Bibliographic record
Abstract
Objectives : This study examined the factors influencing variation by local areas of antibiotics prescription rate in upper respiratory infections (URI) according to the public reporting. Methods : We used the National Health Insurance Claims Data which the clinics claimed for URI (Korean Standard Classification of Disease, J00 ~ J06) in ambulatory care. The period of analysis was from the first quarter (from January to March) of 2005 to the first quarter of 2007. The number of samples was total 242 local areas that included all clinics (N = 7,942), which prescribed antibiotics for URI in ambulatory care. Results : None of the demographic and socioeconomic characteristic indicators was statistically significant. Among the provider factors, An increase in number of doctors and the average annual antibiotics prescription rate (from 2003 to 2004) for URI by local area were significantly related to an increase of antibiotics prescription rate according to the public reporting. And an increase in number of pediatric clinics, the proportion of clinics less than 5 years since has opened and the average annual fluctuation of antibiotics prescription rate (from 2003 to 2005) were significantly related to a decrease in antibiotics prescription rate by local area according to the disclosure of information. Conclusions : According to the public reporting, the antibiotics prescription rate in clinics had decreased sharply. However, the reduction of antibiotic prescription rate varied in different local areas. The factors influencing variation by local areas in antibiotics prescription rate can be used for establishing effective strategies to reduce variation by region in antibiotics prescription rate.
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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 | 0.007 |
| 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 | 0.001 | 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".