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Record W4288683171 · doi:10.18773/austprescr.2022.031

The anticholinergic burden: from research to practice

2022· review· en· W4288683171 on OpenAlex
Sarah N. Hilmer, Danijela Gnjidic

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAustralian Prescriber · 2022
Typereview
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsInstitute of Aging
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsAnticholinergicDeprescribingMedicineAdverse effectIntensive care medicineAnticholinergic agentsDrugPsychiatryPharmacologyPolypharmacy

Abstract

fetched live from OpenAlex

Drugs with anticholinergic effects are known to cause adverse effects such as dry mouth, constipation and urinary retention. In older people drugs with anticholinergic effects may contribute to cognitive decline and a loss of functional capacity. Many drugs that are not in the anticholinergic drug class also have anticholinergic effects. They include antidepressants, antipsychotics and antihistamines. Taking multiple drugs with anticholinergic effects creates an anticholinergic burden. It is important that clinicians identify which patients are at risk. There are several tools to assess the anticholinergic burden. Clinicians can use these tools to make a pharmacological risk assessment when reviewing a patient's medicines. This can assist decisions about continuing or stopping drugs with anticholinergic effects. Deprescribing drugs with anticholinergic effects has several potential benefits in older people. In addition to reversing adverse effects, deprescribing may prevent problems such as falls.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.620
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.296
GPT teacher head0.510
Teacher spread0.213 · 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