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Record W1603165824 · doi:10.1111/add.12423

Considering prescription opioid‐related harms in the population: Response to commentaries

2014· letter· en· W1603165824 on OpenAlexafffundabout
Benedikt Fischer, Annette Keates, Gerhard Bühringer, Jens Reimer, Jürgen Rehm

Bibliographic record

VenueAddiction · 2014
Typeletter
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsPublic Health OntarioUniversity of TorontoSimon Fraser UniversityFraser HealthCentre for Addiction and Mental Health
FundersCanadian Institutes of Health ResearchPublic Health Agency of Canada
KeywordsMedical prescriptionHarmPopulationMedicineHealth careChronic painDemographyPsychologyPsychiatrySocial psychologyEnvironmental healthNursingPolitical scienceSociology

Abstract

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The commentaries to our recent paper 1 make some important and insightful points to which we briefly respond. Rintoul et al. 2 correctly point out that little is known from a population health—or social determinants—perspective about medical prescription opioid (PO) use and PO-related harms. Specifically, there are few data on how basic social variables such as socio-demographics, education, socio-economic status, etc. influence the above phenomena. Such gradients are fairly well explored for other legal and illegal drug use 3, 4; a recent examination of non-medical PO use among Ontario adults and students found a broad distribution across socio-economic classes 5. The empirical situation is quite different with regard to another population level-effect (as raised by Samet & Tsui 6); namely, the correlation between levels of PO dispensing and corresponding levels of harms in a population. Several studies from different North American jurisdictions have documented that the levels of non-medical PO use, as well as PO-related morbidity and mortality outcomes, covary notably with PO dispensing levels 7, 8; this may imply that meaningfully lowered PO dispensing levels result in lower levels of harm outcomes; a key question is, however, on how to achieve such reductions without undermining pain care access and quality. An important issue, here—also in light of the limited evidence on the effectiveness of PO therapy for chronic pain—is a focus upon high-dose PO prescribing, accounting for large shares of overall POs consumed yet also documented as a key determinant of PO harms (e.g. mortality) 9, 10. Clinically sensible reductions, especially in high PO dosing, may bring ways to substantively reduce overall PO consumption, and harms, in populations, and relevant mechanisms have been proposed (e.g. registration for high doses) 11. Another key area, especially in PO-rich North America, is the widespread and generous use of POs for problems where alternative interventions may be equally beneficial yet coming with fewer risks. Griffiths et al. 12 point out correctly that both in terms of PO use environments and problems, the European Union (EU) ought to be considered a heterogeneous entity, featuring substantive interjurisdictional differences 13. This compares to North America, where extensive differences exist in PO use or PO-related problems between provinces (Canada) or states (United States 14, 15). A key question for the EU region is whether PO-related problems there are (i) delayed, (ii) insufficiently noticed or (iii) simply not occurring at levels they are in North America, and what potential determinants for these realities are, as has been observed for other drug issues (e.g. cocaine/crack 16). This question is of particular interest, given that numerous EU jurisdictions have also recently seen substantive increases in overall PO dispensing levels. Finally, the question of effective policy measures is raised. There is still only limited evidence regarding the impacts of prescription monitoring programmes (PMPs), especially with regard to reducing PO-related harms 17, 18; in addition, clinical PO use guidelines are increasingly present, yet little is known about their impact on PO-related harms 19. These areas urgently require attention from rigorous research. None. B.F. and J.R. acknowledge several Canadian Institutes of Health Research (CIHR) grants (specifically grant numbers 1234 and 5678) which supported the present work. B.F. also acknowledges salary and research support from a CIHR/PHAC Applied Public Health Research Chair.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.186
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.270
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2014
Admission routes3
Has abstractyes

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