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Record W2795329345 · doi:10.5770/cgj.21.268

Medications & Polypharmacy Influence on Recurrent Fallers in Community: a Systematic Review

2018· review· en· W2795329345 on OpenAlexaffvenue
Yu Ming, Aleksandra Zecevic

Bibliographic record

VenueCanadian Geriatrics Journal · 2018
Typereview
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsPolypharmacyMedicineDeprescribingConfoundingDepression (economics)Medical prescriptionIntervention (counseling)Injury preventionPoison controlGerontologyPsychiatryIntensive care medicineMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this systematic review is to summarize information about the impact different classes of medications and polypharmacy have on recurrent falls, defined as two or more falls in a 12-month period, in community-dwelling older adults. After adjustment for confounders such as age, gender, weight or depression symptoms, the reviewed studies suggested that older adults who use antidepressants, sedatives or hypnotics and anti-epileptics were more likely to experience recurrent falls than non-users. Polypharmacy (use of four or more prescription medications daily) caused 1.5-2 times higher possibility of recurrent falls in older adults. As a high-risk group, recurrent fallers require meaningful intervention. Medications are believed to be a modifiable risk factor in falls prevention; hence, special consideration should be taken to balance the benefit and harm in initiating, continuing or increasing certain classes of medications in elderly recurrent fallers.

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)0.002
metaresearch head score (Gemma)0.015
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: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0060.008
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.093
GPT teacher head0.437
Teacher spread0.344 · 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

Citations59
Published2018
Admission routes2
Has abstractyes

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