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Record W4402921508 · doi:10.3233/adr-240025

Are Opioids Agitating? A Data Analysis of Baseline Data from the STAN Study

2024· article· en· W4402921508 on OpenAlexafffund
Myriam Lesage, Karin Cinalioglu, Sabrina Chan, Sanjeev Kumar, Tarek K. Rajji, Ashley Melichercik, Carmen Desjardins, Jess Friedland, Amer M. Burhan, Sarah Colman, Li Chu, Simon Davies, Peter Derkach, Sarah Elmi, Philip Gerretsen, Ariel Graff‐Guerrero, Maria Hussain, Zahinoor Ismail, Donna Kim, Linda Krisman, Rola Moghabghab, Benoit H. Mulsant, Bruce G. Pollock, Aviva Rostas, Lisa Van Bussel

Bibliographic record

VenueJournal of Alzheimer s Disease Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsQueen's UniversityOntario Shores Centre for Mental Health SciencesWestern UniversityHotchkiss Brain InstituteDouglas Mental Health University InstituteMcGill UniversityUniversity of CalgaryCentre for Addiction and Mental HealthUniversity of TorontoJewish General Hospital
FundersFondation Brain Canada
KeywordsDementiaAcetaminophenMedicineOpioidPain managementPopulationChronic painPhysical therapyAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Agitation, a common dementia symptom often arising from untreated pain, lacks comprehensive research on its connection with opioids prescribed for long-term pain. This study investigated the relationship between opioid use and agitation in dementia patients. Participants ( n = 188) were categorized into opioid, acetaminophen PRN, or no-pain medication groups. Despite higher reported pain levels in the opioid group, no significant differences in agitation were observed among the groups. In conclusion, opioid use for pain management in older adults with dementia did not significantly impact agitation, emphasizing the ongoing importance of proper pain management in improving dementia care and addressing agitation in 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)0.005
metaresearch head score (Gemma)0.013
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 score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.115
GPT teacher head0.386
Teacher spread0.271 · 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 routes2
Has abstractyes

Explore more

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