Prevalence of adverse drug reactions with commonly prescribed drugs in different hospitals of Kathmandu valley.
Bibliographic record
Abstract
OBJECTIVES: To study the prevalence of adverse drug reactions (ADRs) in five different hospitals of Kathmandu Valley. MATERIALS AND METHODS: An analytical cross sectional study was designed from May 2007 to September 2007 in which prevalence of ADR was calculated. A total of 37 cases of ADRs were taken from 4287 patients and 10% of the remaining population without ADRs i.e. 425 out of 4250 patients was selected randomly. ADRs were analyzed as per the structured questionnaires designed by Canadian adverse drug reaction monitoring program. Data thus obtained were analyzed by using SPSS and Excel 2003 software and relevant statistical tools were applied. RESULTS: Prevalence of ADR in this study was 0.86% and male to female ratio was 0.85. 54.1% were female and 45.9% were male (P = 0.65). The highest percentage of ADRs were seen in adult patients, however the difference was statistically not significant. Maximum numbers of ADRs were reported from skin, 35.13% followed by GIT, 29.72% and then from CNS, 18.91%. Anti-infectives were associated with maximum number of ADRs followed by IV urograffin. Rashes, 35.13% were the most common type of ADRs reported followed by vomiting, 13.51% and then dizziness which was 10.81%. Regarding the outcomes attributed to ADRs, one patient died due to ADR caused by dapsone and 15 cases got hospitalized due to ADRs. The incidence of ADRs in different age groups was not significant. Similarly, there was no significant association between ADRs and sex. No significant difference was seen in case of age group less than one year as compared to two or more years of age (P = 0.78). For causality of ADRs, according to Naranjo algorhythm scale, 35% of reactions were assessed to be probable, 32% as possible and 19% were definite. Similarly, for severity assessment, 54% reports were mild, 35% were moderate and 10.81% were severe. CONCLUSION: Prevalence of ADR in this study was 0.8% which is similar to other studies in other countries. All the ADRs were not toxic reactions and they were unpredictable.
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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".