The inappropriate use of proton pump inhibitors and its associated factors among community-dwelling older adults
Bibliographic record
Abstract
OBJECTIVES: Little is known about the inappropriate use of proton pump inhibitors (PPIs) and how mild cognitive impairment (MCI) and high comorbid burden relate to the inappropriate prescribing of PPIs. Therefore, the current study aimed to examine these associations among community-dwelling older adults in Jordan. METHOD: This cross-sectional study was conducted on 215 community-dwelling older adults from three local healthcare centers located in Irbid, Jordan. Data about PPI use, including the name of medication, dose, frequency, duration, and indication, were collected retrospectively from a review of the participating older adults' medication cards for November and December 2019. The collected data were evaluated based on the Food and Drug Administration (FDA) guidelines. MCI was measured using the Arabic version of the Montreal Cognitive Assessment, and comorbid burden was measured using the Cumulative Illness Rating Scale for Geriatrics. RESULTS: Forty-seven percent of the participants were found to have taken a PPI, with 68 % having taken one for a longer period than recommended by the FDA. Older adults with MCI or high comorbid burden were found to be more susceptible than other older adults to the long-term use of PPIs. The logistic regression revealed that MCI is a statistically significant predictor of inappropriate PPI use (p < 0.001). CONCLUSION: Inappropriate PPI use is common among community-dwelling older adults in Jordan, with a significantly higher prevalence of inappropriate PPI use in people with MCI than in people with normal cognitive abilities. Future intervention studies are highly recommended to encourage optimal prescribing of PPIs for community-dwelling older adults.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".