Chronic Pain and Posttraumatic Stress Among Patients in Substance Use Treatment: Protocol for NOR-APT, a Longitudinal Cohort Study
Bibliographic record
Abstract
BACKGROUND: Chronic pain conditions and posttraumatic stress disorder (PTSD) are highly prevalent among patients with substance use disorders (SUDs). Both can impact outcomes of SUD treatment and quality of life. There is a need for a large-scale study on the overlap of and interactions between SUD, chronic pain, and PTSD. OBJECTIVE: The Norwegian Addiction, Pain and Trauma study (NOR-APT) is the first longitudinal study to describe how substance use and outcomes of SUD treatment are impacted by (1) chronic pain and pain characteristics and (2) interactions between comorbid chronic pain and PTSD. METHODS: Self-reported questionnaire data were collected from patients in all types of SUD treatment at four hospital sites in Norway. The questionnaire data on substance use, pain, and posttraumatic stress symptoms will be combined with retrospective and prospective longitudinal data from high-quality demographic and health registries. The registry data cover, for example, treatment episodes, diagnoses, and prescribed medications and socioeconomic variables, with a follow-up period of altogether 20 years (approximately 2008-2029). RESULTS: Questionnaire data were collected during March 2021-June 2024. Altogether 1890 patients were approached, and 1645 (87%) questionnaires were completed. The estimated final sample size pending data cleaning (eg, removal of duplicates and validation of consent forms) is 1400-1500. Linkage of registry data for approximately 1000 with valid ID numbers and consent is planned for 2026 (retrospective) and 2029/2030 (prospective). At least 10 publications are planned in the period 2025-2028, based on funding received from the Foundation Dam, the Norwegian Research Council, Akershus University Hospital, and Oslo University Hospital. We plan to apply for further funding related to the use of the prospective registry data. CONCLUSIONS: Results from the NOR-APT study will contribute to a better understanding of SUD, chronic pain and PTSD comorbidities, their interactions, trajectories and impact on SUD treatment outcomes, and subjective quality of life. Results can also contribute knowledge toward the development and assessment of treatment interventions that can improve SUD treatment outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT04908410; https://clinicaltrials.gov/study/NCT04908410. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67663.
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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 | 0.029 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".