EVALUATION OF GERIATRIC PRESCRIPTIONS APPLYING BEERS CRITERIA IN A TERTIARY CARE HOSPITAL
Bibliographic record
Abstract
Aim: To assess the geriatrics prescription applying beer's criteria in a tertiaryObjective: To improve the quality of life of elderly patients by reducing the prescriptions of potentially inappropriate medicines and polypharmacy as per beers 2012 criteria.Methodology: A prospective observational study was conducted over a period of 8 months at a tertiary care hospital in Hyderabad, on 150 patients admitted to various collected data was then evaluated using beers criteria Results: Out of the total 150 patients participated in the study, 97 (64.6%) were males and 53 (35.4%) were females.79 patients were reported to be rec patients were overprescribed as per medication appropriate index (MAI).Conclusion: A higher proportion of patients involved in the study were found to be associated with polypharmacy and inappropriate prescriptions.There is a need to make prescribers aware of the irrational prescribing among the geriatric patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".