Quality of primary care among individuals receiving treatment for opioid use disorder.
Bibliographic record
Abstract
OBJECTIVE: To determine if people receiving opioid agonist treatment (OAT), a long-term treatment approach, are also receiving high-quality primary care. DESIGN: Retrospective cohort study. SETTING: Ontario. PARTICIPANTS: Recipients of public drug benefits who had at least 6 months of continuous use of methadone or buprenorphine between October 1, 2012, and September 30, 2013. MAIN OUTCOME MEASURES: Rates of cancer screening and diabetes monitoring among those who had at least 6 months of continuous OAT were compared with matched controls. Conditional logistic regression models were used to assess differences after adjusting for confounders. In secondary analyses, outcomes by type of OAT and factors related to health care delivery were compared. RESULTS: A cohort of 20 406 OAT patients was identified; they had a mean (SD) of 31 (15) physician clinic visits during the 6-month study period. Compared with the control group, OAT patients were less likely to receive screening for cervical cancer (48.7% vs 62.6%; adjusted odds ratio [AOR] of 0.34, 95% CI 0.31 to 0.36), breast cancer (23.3% vs 49.1%; AOR = 0.19, 95% CI 0.16 to 0.24), and colorectal cancer (32.5% vs 49.0%; AOR = 0.34, 95% CI 0.30 to 0.38), and less likely to have monitoring for diabetes (11.7% vs 28.5%; AOR = 0.16, 95% CI 0.13 to 0.21). Patients receiving OAT who were taking buprenorphine, enrolled in a medical home, or seeing a low-volume prescriber were generally more likely to receive cancer screening and diabetes monitoring. CONCLUSION: Patients receiving OAT were less likely to receive chronic disease prevention and management than matched controls were despite frequent health care visits, indicating a gap in equitable access to primary care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".