EVALUATION OF USE OF ANALGESICS IN PAIN MANAGEMENT AMONG SURGEONS IN A TERTIARY CARE HOSPITAL
Bibliographic record
Abstract
Objective: The objective of this study is to evaluate pain and to assess if analgesic prescriptions are according to the World Health Organization guidelines. Methods: The study was conducted in the Department of Surgery in a tertiary care hospital. Patients with age >18 years, of either sex, admitted to surgery ward were included in the study. Pain assessment was done using a visual analog scale and McGill questionnaire. Information obtained from case paper sheets was recorded, such as name of analgesics, the generic name of prescribed analgesics, dosage, route of administration, frequency, number of analgesics per prescription, and non-pharmacological techniques. Data generated from the questionnaire were entered into an Excel sheet, and percentages were calculated. Results: A total of eight different analgesics were prescribed in the study group. Paracetamol was the maximally prescribed drug (40%). In 48% of cases, antacids were given along with analgesics. A majority of analgesics were prescribed in generic names (52%). No drug was prescribed to almost 18% cases even though the pain intensity was of mild-to-moderate intensity. Conclusion: Commonly prescribed drugs were paracetamol + tramadol. Prescription pattern of analgesics is partially deviating from standard guidelines. Generic names were written in the majority of prescriptions, which is in accordance with standard prescription writing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".