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Polypharmacie et médicaments inappropriés chez les patients âgés multimorbides. Ce que l’étude OPERAM nous apprend et va nous apprendre

2022· article· fr· W4221115630 on OpenAlexaff
Lisa Bretagne, Katharina Tabea Jungo, Manuel R. Blum, Matthias Schwenkglenks, Arnaud Chioléro, Cinzia Del Giovane, Bariş Gencer, Drahomir Aujesky, Nicolas Rodondi

Bibliographic record

VenueRevue Médicale Suisse · 2022
Typearticle
Languagefr
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineGynecologyMedical prescriptionMulticenter studyLife expectancyPopulationRandomized controlled trialInternal medicinePharmacology

Abstract

fetched live from OpenAlex

Polypharmacy and inappropriate medication use are very common in multimorbid older patients. This population has unfortunately been excluded from most large, randomized studies. In a recent multicenter randomized study (OPERAM), we included over 2000 multimorbid patients. We found that 86% of the patients aged 70 years and more had inappropriate medications and that these medications could be discontinued without negative impact on the health of these patients. This cohort of multimorbid patients will be followed for 10 years to evaluate their prognosis, life expectancy, treatments and quality of life, with numerous projects to better understand the inappropriate prescribing of individual drugs and their consequences on the health of this population.

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)0.008
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.084
GPT teacher head0.375
Teacher spread0.291 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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