A Call for Consensus in Defining Efficacy in Clinical Trials for Opioid Addiction: Combined Results from a Systematic Review and Qualitative Study in Patients Receiving Pharmacological Assisted Therapy for Opioid Use Disorder
Bibliographic record
Abstract
Abstract Background Given the complex nature of opioid addiction treatment and the rising number of available opioid substitution and antagonist therapies (OSAT), there is no ‘gold standard’ measure of treatment effectiveness, and each successive trial measures a different set of outcomes which reflect success in arbitrary or opportune terms.We sought to describe the variation in current outcomes employed across clinical trials for opioid addiction, as well as determine whether a discrepancy exists between the treatment targets that patients consider important and how treatment effectiveness is measured in the literature. Methods We searched nine commonly used databases (e.g. EMBASE, MEDLINE) from inception to August 1, 2015. Outcomes used across trials were extracted and categorized according to previously established domains. To evaluate patient reported goals of treatment, semi-structured interviews were conducted with 18 adults undergoing methadone treatment. Results We identified 60 trials eligible for inclusion. Once outcomes were categorized into eight broad domains (e.g. abstinence/substance abuse), we identified 21 specific outcomes with furthermore 53 subdomains and 118 measurements. Continued opioid use and treatment retention were the most commonly reported measures (46%, n=28). The majority of patients agreed that abstinence from opioids was a primary goal in their treatment, however they also stressed goals under-reported in clinical trials. Conclusion There is inconsistency in the measures used to evaluate the effectiveness of OSATs. Individual and population level decision making is being guided by a standard of effect considered useful to researchers yet in direct conflict with what patients deem important. PROSPERO ID CRD42013006507
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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 | 0.764 | 0.808 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.009 |
| Bibliometrics | 0.028 | 0.021 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.023 | 0.047 |
| Open science | 0.010 | 0.025 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".