Safety Assessment of Potentially Inappropriate Medications Use in Older People and the Factors Associated with Hospital Admission
Bibliographic record
Abstract
PURPOSE: Potentially Inappropriate Medications (PIM) use in elderly people may be responsible for the development of Adverse Drug Reaction (ADR) which, when severe, leads to hospital admissions. OBJECTIVES: to estimate the prevalence of elderly who had used PIM before being admitted to hospital admission and to identify the risk factors and the hospitalizations related to ADR arising from PIM. METHODS: A descriptive and cross-sectional study was performed in the internal medicine ward of a teaching hospital (Brazil), in 2008. With the aid of a validated form, patients aged ≥ 60 years, with length of hospital stay ≥ 24 hours, were interviewed about drugs taken prior to the hospital admission and the complaints/reasons for hospitalization. RESULTS: 19.1% (59/308) of older patients had taken PIM before hospital admission and in 4.9%; there were a causal relation between the PIM taken and the complaint reported. PIM responsible for admissions were: amiodarone, amitriptyline, cimetidine, clonidine, diazepam, digoxin, estrogen, fluoxetine, lorazepam, short-acting nifedipine and propranolol. 47.0% of the clinical manifestations of PIM-related ADR were: dizziness, fatigue, digoxin toxicity and erythema. Only polypharmacy was detected as a risk factor for the occurrence of ADR of PIM (p = 0.02). CONCLUSION: PIM use in elderly people is not a risk factor for ADR-related hospital admission. Probably, severe ADR, which lead to hospitalizations of older people, can be explained by idiosyncratic response or the predisposition of these patients to develop adverse drug events, whether or not drugs are classed as PIM.
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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 | 0.005 |
| 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".