The Use of Expertise-Based Randomized Controlled Trials to Assess Spinal Manipulation and Acupuncture for Low Back Pain
Bibliographic record
Abstract
In Brief Study Design. Systematic review. Objective. To assess current use of expertise-based randomization in trials of acupuncture or spinal manipulation for low back pain. Summary of Background Data. The randomized clinical trial is often referred to as the gold standard for providing evidence to guide therapeutic decisions. Random allocation of participants to intervention and control groups theoretically should balance these groups for both known and unknown prognostic factors; however, when randomizing patients to competing interventions in which the clinician's skill is a central aspect of the intervention, (e.g., surgery, chiropractic, rehabilitation) a differential expertise bias may exist if a majority of clinicians participating have greater expertise in 1 of the 2 interventions under evaluation. Randomizing patients to therapists experienced in the interventions under investigation can overcome this bias. Methods. We systematically identified relevant randomized controlled trials published up to December 2005. Two independent reviewers extracted data in duplicate using a standardized form. Results. Of 12 eligible trials, none made use of an expertise-based randomized trial design. Conclusion. Investigators designing acupuncture or spinal manipulation trials in which 2 or more active therapies are compared should consider expertise-based randomization to increase the validity and feasibility of their efforts. Expertise-based randomization may decrease bias in nonpharmacological RCTs. We conducted a systematic review of RCTs exploring the efficacy of acupuncture or spinal manipulation for low back pain to investigate the use of expertise-based design. Twelve RCTs were identified that compared competing therapist-administered therapies; none made use of expertise-based randomization.
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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 | 0.305 | 0.584 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.028 | 0.019 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".