An Evaluation of Potential Inappropriate Prescribing Among Older Persons in Nigeria
Bibliographic record
Abstract
Potential Inappropriate prescribing (PIP), Drug-Drug interactions (DDI) and polypharmacy are major risk factors for adverse drug reactions among older persons. Although these factors in many cases co-exist in prescriptions to older persons, very few studies have evaluated the inter-relationship between these factors concomitantly. This study aimed to evaluate PIP and DDI and to determine the associations between PIP, DDI, and polypharmacy among Nigerian older persons. This study was a retrospective evaluation of medicine utilization among older persons at Olabisi Onabanjo University Teaching Hospital, Nigeria, using a medical chart review. Older persons aged ≥60 years, with chronic diseases that attended the medical outpatient clinics of the hospital between 1st January and 31st December 2016 were included. Eligible patients’ records were randomly sampled. Information including patients’ demographics, medical and medication histories and current medications were extracted with a checklist. PIP and DDI were evaluated using the 2015 updated Beers Criteria. Associations were determined using Chi-squared test and a binary logistic regression. A total of 352 participants, mean age 69.03±7.35 years were evaluated. According to the Beers Criteria, PIP and DDI among the participants were (124/352, 35.2%) and (20/352, 5.7%) respectively. Majority of the participants (192/352, 54.5%) received polypharmacy. A few participants (12/352. 3.4%) received prescriptions containing PIP, DDI, and polypharmacy concomitantly. DDI was significantly associated with PIP in a logistic regression (OR=0.18, 95%CI=0.05-0.68, p=0.01) and with polypharmacy in a Chi-squared test (OR=3.55, 95%CI=1.16-10.83, p=0.02). This study concludes that PIP, DDI and polypharmacy are interrelated and should be considered when prescribing to older persons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".