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Record W2134190413 · doi:10.1377/hlthaff.2012.0846

A Call For Evidence-Based Medical Treatment Of Opioid Dependence In The United States And Canada

2013· article· en· W2134190413 on OpenAlexaffabout
Bohdan Nosyk, M. Douglas Anglin, Suzanne Brissette, Thomas Kerr, David C. Marsh, Bruce R. Schackman, Evan Wood, Julio Montaner

Bibliographic record

VenueHealth Affairs · 2013
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsMinistry of Energy, Northern Development and MinesHôpital Saint-LucAIDS VancouverNOSM UniversitySimon Fraser University
FundersNational Institute on Drug Abuse
KeywordsBuprenorphineMethadoneCopaymentMedicineOpioidPatient Protection and Affordable Care ActPublic healthHeroinHealth insuranceMedical prescriptionBusinessPublic economicsHealth carePsychiatryDrugNursingEconomic growthEconomics

Abstract

fetched live from OpenAlex

Despite decades of experience treating heroin or prescription opioid dependence with methadone or buprenorphine--two forms of opioid substitution therapy--gaps remain between current practices and evidence-based standards in both Canada and the United States. This is largely because of regulatory constraints and pervasive suboptimal clinical practices. Fewer than 10 percent of all people dependent on opioids in the United States are receiving substitution treatment, although the proportion may increase with expanded health insurance coverage as a result of the Affordable Care Act. In light of the accumulated evidence, we recommend eliminating restrictions on office-based methadone prescribing in the United States; reducing financial barriers to treatment, such as varying levels of copayment in Canada and the United States; reducing reliance on less effective and potentially unsafe opioid detoxification; and evaluating and creating mechanisms to integrate emerging treatments. Taking these steps can greatly reduce the harms of opioid dependence by maximizing the individual and public health benefits of treatment.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.817

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 integrity0.0000.000
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 head0.031
GPT teacher head0.315
Teacher spread0.284 · 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

Citations126
Published2013
Admission routes2
Has abstractyes

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