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Record W4387025519 · doi:10.1136/spcare-2023-004583

Drug dependence epidemiology in palliative care medicinal cannabis trials

2023· article· en· W4387025519 on OpenAlexaboutno aff
Chee Yen Lee, Phillip Good, Georgie Huggett, Ristan M. Greer, Janet Hardy

Bibliographic record

VenueBMJ Supportive & Palliative Care · 2023
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
FundersMater Foundation
KeywordsMedicineCannabisPalliative careDrugOpioidDistressPsychiatryInternal medicineClinical psychologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: Drug dependence is becoming increasingly common and meeting palliative care patients with substance use disorders is inevitable. However, data on substance use in these patients are lacking. This study aims to evaluate the prevalence of drug dependence in palliative care patients with advanced cancer and correlate with symptom distress and opioid use. METHODS: Palliative care patients with advanced cancer interested in participation in a medicinal cannabis trial were required to complete Alcohol, Smoking and Substance Involvement Screening Test (ASSIST), Edmonton Symptom Assessment Scale (ESAS) and record of concomitant medications including baseline opioid use as part of the eligibility screen. RESULTS: Of the 182 participants, 167 (92%) reported lifetime alcohol and 132/182 (73%) lifetime tobacco use. No participant reached the threshold criteria for high risk of drug dependence with majority being low risk. There was no correlation between ASSIST score, ESAS and oral morphine equivalent. CONCLUSION: This study identified alcohol and tobacco as the main substances used in this group of patients and that most were of very low risk for drug dependence. This suggests routine drug screening for palliative care patient may not be justified, but the high possibility of questionnaire bias is acknowledged.

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 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)0.005
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.148
GPT teacher head0.475
Teacher spread0.327 · 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 teacher head, not a consensus.

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
Published2023
Admission routes1
Has abstractyes

Explore more

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