A Prospective Study on Adverse Drug Reactions in an Indian Hospital
Bibliographic record
Abstract
Background:ADRs have a major impact on public health, reducing patients’ quality of life and imposing a considerable financial burden on the health care systems. Objectives: The main objectives were to analyze the pattern and extent of occurrence of ADRs in the hospital, identify co-morbidities, past and present illness, assess causality and identify the offending drugs, assess the severity and preventability of adverse drug reactions. Methods:Prospective, observational, spontaneous, reporting study with both active and passive methods. Results:Thestudy was carried out from January 2009 to August 2012. A total of 950 ADRs were accepted from 1227 reported ADRs. Female patients experienced more number of ADRs when compared to male patients. Fever was the most commonly observed reason for admission. Maculopapular skin rashes were the commonly observed ADR in the study population. Amoxicillin and clavulenic acid combination implicated more number of ADRs in the antibiotic category than others. Sixty one percent of the ADRs were moderate in severity followed by minor and severe ADRs. Most of the reactions in this study population were managed by withdrawing the offending drug and rechallenge was performed in few subjects. Most of the ADRs were definitely preventable (40%) and were predictable in nature. Eighty percent of the reactions were probably related to offending drugs, 758 reactions were likely to cause ADRs. Twenty five percent ADRs were treated symptomatically in the study population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".