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Record W2769008184 · doi:10.9778/cmajo.20170104

Changes in the dispensing of opioid medications in Canada following the introduction of a tamper-deterrent formulation of long-acting oxycodone: a time series analysis

2017· article· en· W2769008184 on OpenAlexafffundvenueabout
Tara Gomes, Andrea Mastorakos, J. Michael Paterson, Ingrid Sketris, Patrícia Caetano, Simón Greaves, David Henry

Bibliographic record

VenueCMAJ Open · 2017
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsMcMaster UniversityUniversity of ManitobaSt. Michael's HospitalGovernment of ManitobaInstitute for Clinical Evaluative SciencesUniversity of TorontoDalhousie University
FundersOntario Ministry of Health and Long-Term Care
KeywordsOxycodoneHydromorphoneFentanylMorphineOpioidMedicinePopulationCodeineAnesthesiaOxymorphonePharmacologyHydrocodoneInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: In February 2012, a reformulated tamper-deterrent form of long-acting oxycodone, OxyNeo, was introduced in Canada. We investigated the impact of the introduction of OxyNeo on patterns of opioid prescribing. METHODS: We conducted population-based, cross-sectional analyses of opioid dispensing in Canada between 2008 and 2016. We estimated monthly community pharmacy dispensing of oral formulations of codeine, morphine, hydromorphone and oxycodone, and a transdermal formulation of fentanyl, and converted quantities to milligrams of morphine equivalents (MMEs) per 1000 population. We used time series analysis to evaluate the effect of the introduction of OxyNeo on these trends. RESULTS: > 0.05 in all provinces). INTERPRETATION: The findings suggest that the introduction of a tamper-deterrent formulation of long-acting oxycodone in Canada, against a background of changing public drug benefits, was associated with sustained changes in selection of long-acting opioids but only small changes in the quantity of long-acting opioids dispensed. This illustrates the limited effect a tamper-deterrent formulation and associated coverage policy can have when other, non-tamper-deterrent alternatives are readily available.

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 score0.261
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.024
GPT teacher head0.310
Teacher spread0.286 · 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

Citations33
Published2017
Admission routes4
Has abstractyes

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