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Record W4394914660 · doi:10.26800/lv-145-supl9-32

Assessing the need for deprescribing benzodiazepines in older adults in primary care: analysis of a Croatian cohort from the EuroAgeism H2020 ESR7 project

2024· article· en· W4394914660 on OpenAlexaboutno aff
Lucija Nežmah, Dora Belec, Margita Držaić, Ingrid Kummer, Iva Bužančić, Maja Ortner Hadžiabdić, Jovana Brkić, Daniela Fialová

Bibliographic record

VenueLiječnički vjesnik · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersHorizon 2020 Framework Programme
KeywordsDeprescribingCroatianPolypharmacyMedicineCohortPrimary careFamily medicineIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Inappropriate and long-term use (longer than 12 weeks) of benzodiazepines (BZD) is associated with unfavourable outcomes, especially in sensitive groups of patients such as the older adults. In order to increase patient safety and improve outcomes, deprescribing should be suggested to patients with prolonged BZD use. Primary aim of this study was to determine the prevalence of BZD use in older adults in Croatian primary health care. Secondary aim was to estimate the need for their deprescribing.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.340
Teacher spread0.317 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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