Evaluation of clinical and inflammatory profile in opioid addiction patients with comorbid pain: results from a multicenter investigation
Bibliographic record
Abstract
BACKGROUND: Chronic pain is the most commonly reported comorbidity among patients with opioid addiction receiving methadone maintenance treatment (MMT), with an estimated prevalence ranging between 30% and 55%. Evidence suggests that patients with comorbid pain are at high risk for poor treatment response, including continued illicit substance use. Due to the important relationship between the presence of pain and illicit substance abuse within the MMT setting, it is imperative that we target our efforts toward understanding the characteristics of this patient population. METHODS: The primary objective of this study was to explore the clinical and inflammatory profile of MMT patients reporting comorbid pain. This multicenter study enrolled patients (n=235) on MMT for the treatment of opioid dependence. Clinical history and blood and urine data were collected. Blood samples were obtained for estimating the serum levels of inflammatory markers (tumor necrosis factor [TNF]-α, interleukin-1 receptor antagonist [IL-1ra], IL-6, IL-8, IL-10, interferon [IFN]-γ and chemokine (C-C motif) ligand 2 [CCL2]). The study objectives were addressed using a descriptive statistical summary and a multivariable logistic regression model constructed in STATA version 12. RESULTS: Among the participants eligible for inclusion (n=235), serum IFN-γ level and substance abuse behavior proved to be important delineating characteristics for the detection of comorbid pain. Analysis of inflammatory profile showed IFN-γ to be significantly elevated among patients reporting comorbid pain (odds ratio [OR]: 2.02; 95% confidence interval [CI]: 1.17, 3.50; P=0.01). Patients reporting comorbid pain were also found to have an increase in positive opioid urine screens (OR: 1.02; 95% CI: 1.00, 1.03; P=0.01), indicating an increase in illicit opioid consumption. CONCLUSION: MMT patients with comorbid pain were shown to have elevated IFN-γ and higher rates of continued opioid abuse. The ability to objectively distinguish between patients with comorbid pain may help to both improve the prediction of poor responders to MMT as well as identify treatment approaches such as anti-inflammatory medications as safe alternatives for MMT patients with comorbid pain.
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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) | 0.000 | 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".