Real-World Effectiveness and Tolerability of Low Dose Naltrexone to Treat Chronic Pain: A Retrospective Cohort Study of One Pain Physician’s Practice
Bibliographic record
Abstract
Hannah Aalto,1 Sara Paul,1 Virginia McEwen2– 4 1Department of Undergraduate Medical Education, Northern Ontario School of Medicine (NOSM) University, Thunder Bay, Ontario, Canada; 2Chronic Pain Management Program, St. Joseph’s Care Group, Thunder Bay, ON, Canada; 3Interventional Pain Service, Thunder Bay Regional Health Sciences Centre, Thunder Bay, ON, Canada; 4Clinical Sciences Division, Northern Ontario School of Medicine (NOSM) University, Thunder Bay, ON, CanadaCorrespondence: Hannah Aalto, Undergraduate Medical Education, Northern Ontario School of Medicine (NOSM) University, Thunder Bay, ON, Canada, Tel +1 807 632 2948, Email haalto@nosm.caPurpose: Low-dose naltrexone (LDN) is an off-label treatment for chronic pain, with evidence supporting its use mainly consisting of small-scale studies. This retrospective cohort review aimed to contribute to this growing body of literature and evaluate the real-world effectiveness of LDN across a range of chronic pain conditions in a single physician’s practice.Patients and Methods: A total of 128 patients prescribed LDN between September 2021 and June 2024 were reviewed independently by two investigators, with 93 meeting inclusion criteria. Patients were categorized into 12 groups based on diagnosis, with fibromyalgia representing the largest subgroup (27 patients). Retrospective cohort data was collected and included patient demographics, adverse effects, dosage, duration of therapy, and reported symptom relief. Descriptive statistics were used to analyze findings.Results: Subjective symptom relief was reported by 53.8% of patients, most commonly improvements in pain (49 patients) and fatigue (25 patients), when taking LDN. The highest response rates were seen in patients with mast cell activation syndromes and arthritis-related conditions (71.4%). Adverse effects occurred in 49.5% of patients, most frequently nausea and fatigue. No serious adverse effects were reported.Conclusion: These findings suggest that LDN may be an effective and well-tolerated treatment option for a range of chronic pain conditions. Further studies, such as randomized controlled trials, are warranted to confirm these results and optimize dosing strategies.Keywords: chronic pain, low dose naltrexone, LDN, pain
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".