Pragmatic Comparative Effectiveness Trials and Learning Health Systems in Pain Medicine
Open accessAbstract
Large randomized clinical trials or aggregates of clinical trials represent the highest levels of clinical evidence because they minimize different sources of confounding and bias. The current review provides an in-depth discussion of the challenges faced and methods we can use to overcome these obstacles to tailor novel designs of pragmatic effectiveness trials to pain medicine. The authors describe their experiences with an open-source learning health system to collect high-quality evidence and conduct pragmatic clinical trials within a busy academic pain center.
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Effective in-house electronic data capture could overcome persistent limitations in corrections settings studies, such as small sample sizes, sampling bias, measurement limitations, and variability in facilitator quality.
“Effective in-house electronic data capture could transcend persistent limitations in corrections settings studies, such as small sample sizes, sampling bias, measurement limitations, and variability in the quality of facilitators.”
From Pain in US corrections settings: the promise of digital solutions for better data and treatment access (2024) · cites this paper as [9]
The section on high-quality, real-world data collection says that a system embedded in routine care can enable larger-scale trials and collect needed outcome measures. The paper discusses pain-medicine studies, not corrections settings, and does not say that electronic data capture would overcome sampling bias or variability in facilitator quality. (LLM, conf=0.86)
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Hettie, G., Nwaneshiudu, C. A., Ziadni, M. S., Darnall, B. D., Mackey, S., & You, D. S. (2021) · Substance Use & Misuse · doi:10.1080/10826084.2021.1958853 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [10]C. G, J. S RE. Large simple trials and knowledge generation in a learning health system. Washington DC: Institute of Medicine; 2013. 24830060
Cited by
Sorted by citations
Banerjee, A., Mackey, S. C., Vest, N., & Darnall, B. D. (2024) · Pain Medicine · doi:10.1093/pm/pnad150
Self-citation
- Authors
- Salmasi, V., Terkawi, A. S., & Mackey, S. C.
- Affiliations
- Stanford Medicine (US)
- Corresponding author
- Salmasi, Vafi
- Venue
- Anesthesiology Clinics
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 41
- Issue
- 2
- Pages
- 503-517
- ISSN
- 1932-2275 (ISSN-L)
- PMC
- PMC10926352
- OpenAlex
- W4362721616
- Cited by
- 16
- References
- 99
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 3 · 2023: 2
- FWCI
- 0.66
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Anesthesiology Clinics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (K23NS120039)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Health Sciences › Medicine › Anesthesiology and Pain Medicine › Pain Management and Opioid Use
- Keywords
- pragmatic trials, comparative effectiveness research, learning health systems, pain medicine, randomized controlled trials, trial design challenges, confounding and bias
- MeSH terms
- Learning Health System, Humans, Informed Consent, Pain, Research Design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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