ReviewPain and therapy2021
Status of N-of-1 Trials in Chronic Pain Management: A Narrative Review.
Review in Pain and therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- The N-of-1 Trials and where to use them in Rheumatology.Mediterranean journal of rheumatology · 2025Review
- Risks and Benefits of Pharmacological Treatment for Pediatric Chronic Non-cancer Pain: When Safety Evidence Lags Behind Prescription Pads.Paediatric drugs · 2025Review
- N-of-1 tests in general practice: pharmacological considerations.The British journal of general practice : the journal of the Royal College of General Practitioners · 2025Article
- Baduanjin exercise for chronic non-specific low back pain: protocol for a series of N-of-1 trials.BMJ open · 2023Article
- N-of-1 Trials in Cancer Drug Development.Cancer discovery · 2023Review
- Engineering Nanoscale Drug Delivery Systems for Pain.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
N-of-1 trials are randomized controlled clinical trials conducted exclusively on a single patient. The ultimate aim of N-of-1 trials is to optimize a strategy in a particular individual. Chronic pain is a common but refractory clinical problem. Its diverse etiologies and broad variations among patients often lead to the requirement of individualizing medicine. Thus, chronic pain represents a classical condition for N-of-1 clinical trials. Studies have indicated that N-of-1 benefits patients with chronic pain, multiple comorbidities, and uncertain variations during therapies; however, this approach it is not yet adopted as the first choice in pain clinics. To dissect the current status of N-of-1 in chronic pain management, as well as the limitations for its implementation, we herein studied all N-of-1 studies related to chronic pain by searching three major databases (PubMed, ClinicalTrial.gov, Cochrane Library) for publications between 1985 and 2020. Of 35 eligibility papers, 19 were selected for analysis. Results confirmed that N-of-1 trials have solved the refractory cases including osteoarthritis, chronic musculoskeletal pain, and neuropathic pain; however, none of the trials dealt with cancer pain. Longer time and more efforts are needed from investigators when carrying out N-of-1 trials, which inevitably result in implementation difficulties. Of note, all recruited trials were conducted in developed countries. As mobile devices have been introduced and protocols improve, renewed interest in the implementation of N-of-1 trials will occur. Collectively, a previously underestimated conflict between "precision medicine" and "poor implementation" has put N-of-1 in a challenging position for chronic pain management.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.