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Record W2990624034

Multidisciplinary Medication Review in Long-Term Care: A Review of Clinical Utility, Cost-Effectiveness and Guidelines [Internet]

2019· review· en· W2990624034 on OpenAlexaboutno aff
Yi‐Sheng Chao, Danielle MacDougall

Bibliographic record

Venuenot available
Typereview
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPolypharmacyDeprescribingMedicineBeers CriteriaPharmacistGeriatricsMultidisciplinary approachHealth careAdverse effectFamily medicineMEDLINELong-term careIntensive care medicinePharmacyPsychiatryPharmacology
DOInot available

Abstract

fetched live from OpenAlex

“Polypharmacy” refers to the use of two or more medications, and commonly refers to the use of five or more medications. Polypharmacy is more prevalent among those aged 65 years and over than in younger populations., Polypharmacy occurs more frequently among those residing in long-term care facilities than those living in communities and can be due to duplicate or redundant medications for similar diseases. The use of multiple medications can lead to toxicity and decrease drug compliance. The adverse effects of certain medications may induce clinicians to prescribe more drugs to treat them. In a 2008 survey that interviewed 3,132 Canadians aged 65 years and over, 27% of the respondents were regularly taking five or more medications and 12% of them had experienced drug-related adverse events, in comparison to 5% of Canadians taking one or two medications.To prevent inappropriate use of medications among the elderly, there are guidelines aiming at deprescribing certain classes of medications, or avoiding potentially inappropriate medications in populations with specific conditions. One prominent example is the Beers criteria last updated by the American Geriatrics Society in 2019. There are recommendations on the medications that should be avoided in general or specific to health conditions. To put these guidelines into practice requires medication reviews to screen and deprescribe the medications among the elderly, particularly those residing in long-term care facilities.Pharmacist-led medication reviews have been implemented in countries such as the US, the UK, and Canada. In a systematic review, pharmacist-led and team-based medication reviews were found to improve the quality of medication use in long-term care facilities. Team-based reviews involve professionals from different disciplines and often consist of pharmacists, clinicians, and nurses., In a 2011 Canadian Agency for Drugs and Technologies in Health report, low-quality evidence from two systematic reviews and two non-randomized studies showed that team-based medication reviews (every three months in one primary study, unspecified in others) were associated with less use of inappropriate medications and better patient health outcomes, in comparison to usual care. However, it remains unclear whether more frequent medication reviews, such as every three months, can improve medication use and reduce adverse events., This report aims to review the evidence regarding the clinical utility and cost-effectiveness of multidisciplinary medication reviews in long-term care facilities, as well as clinical guidelines on multidisciplinary medication reviews.

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)0.010
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0090.010
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.565
GPT teacher head0.637
Teacher spread0.072 · 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 designSystematic review
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

Citations1
Published2019
Admission routes1
Has abstractyes

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