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Potentially inappropriate medications in geriatric outpatients with polypharmacy: application of six sets of published explicit criteria

2011· article· en· W1492930497 on OpenAlexaboutno aff
Chirn‐Bin Chang, Jen‐Hau Chen, Chiung‐Jung Wen, Hsu‐Ko Kuo, I‐Shu Lu, Lee‐Shu Chiu, Shwu‐Chong Wu, Ding‐Cheng Chan

Bibliographic record

VenueBritish Journal of Clinical Pharmacology · 2011
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPolypharmacyMedicineBeers CriteriaGeriatricsDrugEmergency medicineFamily medicineIntensive care medicinePsychiatry

Abstract

fetched live from OpenAlex

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Many different criteria have been developed to detect potentially inappropriate medications (PIMs), with wide variations in prevalence estimates and inconsistent associations with health outcomes. • Without head‐to‐head comparisons, it is difficult to know whether some PIM criteria systematically detect more or fewer PIMs than others in the same cohorts. WHAT THIS STUDY ADDS • Six sets of PIM criteria were applied to a single cohort, with PIM prevalence ranging from 24 to 73%. • Criteria with a higher number of statements and a higher percentage of local market/institution drug availability tended to detect more PIMs. • Caution should be exercised in applying PIM criteria developed in other regions when medication availability in the local market is limited. AIM Our aim was to compare the practicability of six different potentially inappropriate medication (PIM) criteria in geriatric outpatients with polypharmacy. METHODS We analysed baseline data from the Medication Safety Review Clinic in Taiwanese Elders (MSRC‐Taiwan) study. The prevalence and correlates of PIMs were determined on the basis of criteria developed in the USA, Canada, France, Norway, Ireland and Thailand. The percentage of PIMs considered as drug‐related problems and the problem‐solving rate are reported. RESULTS In the 193 participants, the prevalence of PIM varied from 24 to 73%. Application of the criteria revealed that a high number of chronic medications was a common risk factor for having at least one PIM. Of the 1713 medications reviewed, 5.6–14.8% were considered PIMs. Only 30–40% of the identified PIMs were reported as drug‐related problems by the MSRC team experts. Criteria with a higher number of statements and a higher percentage of local market/institution drug availability tended to detect more PIMs. CONCLUSIONS The prevalence of PIM varied significantly when different criteria were applied. Caution should be exercised in applying PIM criteria developed in other regions when medication availability in the local market is limited.

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 imitation

Not 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.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.124
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.124
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.118
GPT teacher head0.456
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations90
Published2011
Admission routes1
Has abstractyes

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