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Interventions to Address Potentially Inappropriate Prescribing for Older Primary Care Patients

2025· review· en· W4411715458 on OpenAlexafffund
Navindra Persaud, Aine Workentin, Amal Rizvi, Tiphaine Pierson, Émilie Bortolussi‐Courval, Kathy Liu, Alexandria Bennett, Nicole Shaver, Becky Skidmore, Niyati Vyas, Róbert Pap, Faris Almoli, Todd C. Lee, Caroline Sirois, Rita McCracken, Louise Papillon‐Ferland, Emily G. McDonald

Bibliographic record

VenueJAMA Network Open · 2025
Typereview
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalUniversity of British ColumbiaUniversité de MontréalUniversité LavalMcGill UniversityUniversity of OttawaUniversity of TorontoMcGill University Health CentreSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchHealth CanadaMcGill University
KeywordsMedicinePsychological interventionFunnel plotMEDLINEMedical prescriptionData extractionSystematic reviewRandomized controlled trialEmergency medicineMeta-analysisPolypharmacyRelative riskGuidelineEmergency departmentIntensive care medicineFamily medicinePublication biasConfidence intervalInternal medicinePsychiatryNursing

Abstract

fetched live from OpenAlex

Importance: Prescriptions for potentially inappropriate medications are common and, by definition, may carry risks that outweigh benefits. Objective: To determine whether interventions to address potentially inappropriate prescribing for older primary care patients are associated with changes in the number of medications prescribed, drug-related harms, hospitalizations, and mortality. Data Sources: MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were searched from inception to September 6, 2024. Study Selection: Randomized clinical trials of interventions to address potentially inappropriate prescribing for older primary care patients (aged ≥65 years) residing in the community or in long-term care facilities, such as nursing homes or assisted-living facilities, were included. Data Extraction and Synthesis: Two researchers independently screened the records and abstracted data using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. Data were pooled using random-effects models. Main Outcomes and Measures: The planned outcomes were the number of medications, nonserious adverse drug reactions, injurious falls, quality of life, medical visits, emergency department visits, hospitalizations, and all-cause mortality. Random-effects meta-analyses were performed using the inverse variance method for similar studies, reporting risk ratios (RRs) or standardized mean differences (SMDs). Heterogeneity was assessed with I2 values, and publication bias was assessed with funnel plots and the Egger regression test. Results: Of the 14 649 records identified, 118 randomized clinical trials (comprising 417 412 patients) were included in this review. Interventions to address potentially inappropriate prescribing were associated with a reduction in the number of medications prescribed (SMD, -0.25 [95% CI, -0.38 to -0.13]), equivalent to approximately 0.5 fewer medications per patient. However, there were no substantial differences in the other outcomes, including nonserious adverse drug reactions (RR, 0.92 [95% CI, 0.58-1.46]), injurious falls (SMD, 0.01 [95% CI, -0.12 to 0.14]), quality of life (SMD, 0.09 [95% CI, -0.04 to 0.23]), medical visits (SMD, 0.02 [95% CI, -0.02 to 0.07]), emergency department admissions (RR, 1.02 [95% CI, 0.96-1.08]), hospitalizations (RR, 0.95 [95% CI, 0.89-1.02]), or all-cause mortality (RR, 0.94 [95% CI, 0.85-1.04]). Conclusions and Relevance: In this systematic review and meta-analysis, interventions to address potentially inappropriate prescribing were associated with reductions in the number of medications prescribed, with no substantial change in other outcomes. These findings suggest that inappropriate prescribing interventions may be implemented to safely reduce the number of medications prescribed to older adults in the primary care setting. Future studies should continue to evaluate these interventions using standardized criteria and consistently report potential harms to support data synthesis and capture key outcomes such as quality of life, hospitalization, and mortality.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.184
GPT teacher head0.459
Teacher spread0.275 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations12
Published2025
Admission routes2
Has abstractyes

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