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Record W2296095435 · doi:10.1002/pds.3989

Determinants of trends in prescription opioid use in British Columbia, Canada, 2005–2013

2016· article· en· W2296095435 on OpenAlexaffabout
Kate Smolina, Emilie J. Gladstone, Steven G. Morgan

Bibliographic record

VenuePharmacoepidemiology and Drug Safety · 2016
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsInstitute of Population and Public HealthUniversity of British Columbia Hospital
Fundersnot available
KeywordsMedicineOxycodoneOpioidHydromorphoneTramadolMedical prescriptionFentanylCodeinePopulationMorphinePharmacoepidemiologyConsumption (sociology)DemographyEnvironmental healthAnesthesiaInternal medicinePharmacologyAnalgesic

Abstract

fetched live from OpenAlex

PURPOSE: To explore the determinants of total opioid consumption in a Canadian province, and to examine patterns of opioid dispensations by sex, age, and income quintile. METHODS: We used population-based administrative data on prescription drug dispensations in British Columbia (BC; population ~4 million). We apply an index-based approach to examine how changes in population exposure, type of opioids used, and intensity of use contributed to changes in total morphine equivalents dispensed per 1000 population. RESULTS: Between 2005 and 2013 in BC, opioid consumption increased by 31%, driven by longer duration of opioid therapy and by an increase in the use of stronger opioids. Consumption increased for oxycodone, hydromorphone, fentanyl, and tramadol; and declined for morphine, codeine, and other opioids. While we did not find large sex and age differences, the total level of opioid consumption was three times as high among individuals in the lowest income quintile compared to those in the highest income quintile. CONCLUSIONS: Our findings on changes in the type of opioids used and changes in intensity of use suggest that modifications to clinical management of patients on opioid therapy may be warranted. Similar drug utilization statistics, derived from drug information systems, can be reproduced in other jurisdictions to enable a better understanding of the opioid crisis. Copyright © 2016 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.064
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.299
Teacher spread0.279 · 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.

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

Citations27
Published2016
Admission routes2
Has abstractyes

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