Does Prescription Length of Buprenorphine Influence Treatment Outcomes in Opioid Use Disorder? A Retrospective Cohort Study from North India
Bibliographic record
Abstract
Buprenorphine (BUP) effectively suppresses non-prescription opioid use and increases treatment retention in opioid use disorder (OUD). However, short prescription length may interfere with treatment retention and recovery. We wanted to examine whether the outcomes of BUP treatment differ in high (HPL up to 4 wk) and low-prescription (LPL 1–2 wk) length groups. We compared time to treatment discontinuation (TD), non-prescription opioid-positive urine screen, buprenorphine-negative urine screen, and self-reported non-prescription opioid use between two different cohorts of LPL (case record: June 2018 to August 2019; n = 105; observation endpoint: 31 October 2019) and HPL groups (case record: June 2020 to Aug 2021; n = 133; observation endpoint: 31 October 2021). We used Kaplan-Meier survival analysis and log-rank tests for between-group comparisons. We used Cox regression analysis to adjust for age, opioid potency, comorbidities, family income, and marital status. Subjects’ age and buprenorphine dose were significantly lower, and the percentage of high-potency opioid users was significantly higher in the LPL group. In the unadjusted survival analysis, the median time to BUP discontinuation in the HPL was longer than that of the LPL [LPL= 22.4 ± 4.3 wk; HPL = 33.1 ± 8.5 wk; χ2(1)= 5.7; p=.02]. The survival distributions of other outcomes did not differ between groups. When adjusted for covariates, neither the prescription length nor other covariates independently predicted any treatment outcome. Higher prescription length might be associated with longer treatment retention. We provide preliminary evidence to support greater flexibility in BUP treatment, enhancing its scalability and attractiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".