An International, Multidisciplinary Consensus Set of Patient-Centered Outcome Measures for Substance-Related and Addictive Disorders
Bibliographic record
Abstract
Background: In 1990, the United States’ Institute of Medicine promoted the principles of outcomes monitoring in the alcohol and other drugs treatment field to improve the evidence synthesis and quality of research. While various national outcome measures have been developed and employed, no global consensus on standard measurement has been agreed for addiction. It is thus timely to build an international consensus. Convened by the International Consortium for Health Outcomes Measurement (ICHOM), an international, multi-disciplinary working group reviewed the existing literature and reached consensus for a globally applicable minimum set of outcome measures for people who seek treatment for addiction. Methods: To this end, 26 addiction experts from 11 countries and 5 continents, including people with lived experience (n = 5; 19%), convened over 16 months (December 2018–March 2020) to develop recommendations for a minimum set of outcome measures. A structured, consensus-building, modified Delphi process was employed. Evidence-based proposals for the minimum set of measures were generated and discussed across eight videoconferences and in a subsequent structured online consultation. The resulting set was reviewed by 123 professionals and 34 people with lived experience internationally. Results: The final consensus-based recommendation includes alcohol, substance, and tobacco use disorders, as well as gambling and gaming disorders in people aged 12 years and older. Recommended outcome domains are frequency and quantity of addictive disorders, symptom burden, health-related quality of life, global functioning, psychosocial functioning, and overall physical and mental health and wellbeing. Standard case-mix (moderator) variables and measurement time points are also recommended. Conclusions: Use of consistent and meaningful outcome measurement facilitates carer–patient relations, shared decision-making, service improvement, benchmarking, and evidence synthesis for the evaluation of addiction treatment services and the dissemination of best practices. The consensus set of recommended outcomes is freely available for adoption in healthcare settings globally.
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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 | 0.345 | 0.327 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.018 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".