Antibiotic‐prescribing patterns in outpatient departments from a tertiary care hospital in Manipur using WHO AWaRe classification
Bibliographic record
Abstract
Background & objectives One of the major consequences of irrational drug use in treating infection is antibiotic resistance. World Health Organization (WHO) introduced the AWaRe (Access, Watch, Reserve) classification to promote the rational use of antibiotics. This study aims to assess the antibiotic-prescribing patterns in outpatient departments from a tertiary care hospital. Methods A cross-sectional study was conducted on the prescriptions collected from outpatient departments of a tertiary care hospital in Manipur from June to July 2024. Prescriptions containing at least one antibiotic were analysed. The prescribed antibiotics were classified using the WHO AWaRe 2023 tool. Data were analyzed using IBM SPSS V 26.0 and presented as descriptive statistics. Results Among the 1,339 prescriptions, 1,451 antibiotics were prescribed: 1,237 (85.2%) prescriptions included only one antibiotic, while 102 (14.8%) prescriptions contained two or more antibiotics. According to WHO AWaRe classifications, 38 per cent of the antibiotics were in the Access group, 37 per cent in the Watch group, one per cent in the Reserve group, and 24 per cent in the Not Recommended group. Only 26.3 per cent of the antibiotics were prescribed by their generic names, while 74 per cent were on the WHO essential medicines list. Interpretation & conclusions The antibiotic prescriptions from the Access group were below the WHO target of 60 per cent, and nearly a quarter involved non-recommended antibiotics.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".