Morbidity profile and prescribing patterns among outpatients in a teaching hospital in Western Nepal
Bibliographic record
Abstract
BACKGROUND: Recent studies on prescribing among outpatients in hospitals in Western Nepal are lacking. The main objectives of the study were to obtain information on the morbidity pattern among outpatients and to analyze prescribing using drug use indicators. METHODS: A retrospective hospital record based study from 01.01.2004 to 31.12.2004 was carried out among individuals attending the outpatient department (OPD) of the Manipal Teaching hospital, Pokhara, Western Nepal. A total of 32,017 new patients attended the OPD during the study period. Systematic random sampling (1 in every 20 patients) was done and 1600 patients selected. After excluding patients visiting the emergency department, those who got admitted and whose records were not available, 1261 cases were analyzed. The demographic details, morbidity pattern, average number of drugs prescribed, percentage of drugs prescribed by generic names and from the Essential drug list of Nepal (Essential drugs are those which satisfy the priority healthcare needs of the population), percentage of encounters with an antibiotic and an injection prescribed were noted. RESULTS: 1261 patients made 1772 visits. Upper respiratory tract infection and acid peptic disease were the most common diagnoses. The mean number of drugs was 1.99. Only 19.5% and 39.6% of drugs were prescribed by generic name and from the Essential drug list. Antibiotics and injections were prescribed in 26.4% and 0.96% of encounters. Cetrizine, vitamins, amoxicillin, the combination of paracetamol and ibuprofen and ranitidine were most commonly prescribed. CONCLUSIONS: Upper respiratory tract infections and acid peptic disease were the common illnesses. Generic prescribing and use of essential drugs were low. Some of the drug combinations being used were irrational. Prescriber education may be helpful in encouraging rational prescribing.
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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 | 0.000 | 0.001 |
| 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".