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Record W2907582570 · doi:10.1377/hlthaff.2018.05162

Medication Treatment For Opioid Use Disorders In Substance Use Treatment Facilities

2019· article· en· W2907582570 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealth Affairs · 2019
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsColumbia College
FundersNational Institute on Drug Abuse
KeywordsMedicaidBuprenorphineOpioid use disorderOddsMedicineEnvironmental healthSubstance abuseOpioid epidemicFamily medicineOpioidPsychiatryMedical emergencyHealth careLogistic regressionEconomic growth

Abstract

fetched live from OpenAlex

Medication treatment (MT) is one of the few evidence-based strategies proposed to combat the current opioid epidemic. We examined national trends and correlates of offering MT in substance use treatment facilities in the United States. According to data from national surveys, the proportion of these facilities that offered any MT increased from 20.0 percent in 2007 to 36.1 percent in 2016-mainly the result of increases in offering buprenorphine and extended-release naltrexone. Only 6.1 percent of facilities offered all three MT medications in 2016. Facilities in states with higher opioid overdose death rates, facilities that accepted health insurance overall (and, more specifically, those that accepted Medicaid in states that opted to expand eligibility for Medicaid), and facilities in states with more comprehensive coverage of MT under their Medicaid plans had higher odds of offering MT. The findings highlight the persistent unmet need for MT nationally and the role of expansion of health insurance in the dissemination of these treatments.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.039
GPT teacher head0.315
Teacher spread0.277 · 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