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Record W4392384326 · doi:10.1016/s2666-7568(24)00007-2

Prevalence of multimorbidity and polypharmacy among adults and older adults: a systematic review

2024· review· en· W4392384326 on OpenAlexafffund
Kathryn Nicholson, Winnie Liu, Daire W. D. Fitzpatrick, Kate Anne Hardacre, Sarah Roberts, Jennifer Salerno, Saverio Stranges, Martin Fortin, Dee Mangin

Bibliographic record

VenueThe Lancet Healthy Longevity · 2024
Typereview
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversity of OttawaImpactUniversité de SherbrookeQueen's UniversityMcMaster UniversityWestern University
FundersCanadian Institutes of Health Research
KeywordsPolypharmacyMultimorbidityMedicinePopulationOlder peopleGerontologyBeers CriteriaEnvironmental healthIntensive care medicine

Abstract

fetched live from OpenAlex

Multimorbidity (multiple conditions) and polypharmacy (multiple medications) are increasingly common, yet there is a need to better understand the prevalence of co-occurrence. In this systematic review, we examined the prevalence of multimorbidity and polypharmacy among adults (≥18 years) and older adults (≥65 years) in clinical and community settings. Six electronic databases were searched, and 87 studies were retained after two levels of screening. Most studies focused on adults 65 years and older and were done in population-based community settings. Although the operational definitions of multimorbidity and polypharmacy varied across studies, consistent cut-points (two or more conditions and five or more medications) were used across most studies. In older adult samples, the prevalence of multimorbidity ranged from 4·8% to 93·1%, while the prevalence of polypharmacy ranged from 2·6% to 86·6%. High heterogeneity between studies indicates the need for more consistent reporting of specific lists of conditions and medications used in operational definitions.

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)0.005
metaresearch head score (Gemma)0.026
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 score0.011
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.007
Bibliometrics0.0100.010
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.079
GPT teacher head0.417
Teacher spread0.338 · 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

Citations199
Published2024
Admission routes2
Has abstractyes

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