Where are the chiropractic clinical outcomes registries? A scoping review
Bibliographic record
Abstract
OBJECTIVE: This scoping review maps chiropractic-specific clinical outcomes registries. INTRODUCTION: Clinical outcomes registries track patient outcomes to improve evidence-based practice and quality of care; however, their role in chiropractic remains unclear. METHODS: This research adhered to Joanna Briggs Institute's scoping review outline and methodology, as well as the PRISMA-ScR guidelines. Five databases were searched on January 9, 2025, with subsequent search of grey literature and citation tracking. Sources were included if they described chiropractic-specific registries that reported clinical outcomes data. Two reviewers independently screened 604 citations, extracting data into Excel. Variables included registry characteristics and clinical outcomes collected. RESULTS: Only one dedicated chiropractic clinical outcomes registry was identified: Spine IQ, launched in 2016 in the US with approximately 50 chiropractors submitting data on over 2000 low back pain patients. Spine IQ collected patient-reported outcome measures including the Oswestry Disability Index, Bournemouth Questionnaire, and the PROMIS physical function measure. By 2018, Spine IQ had completed its pilot phase and planned expansion to 100 clinics. Three sources were excluded: one spine registry not collecting chiropractic outcomes and two databases that included chiropractic data in publications but did not qualify as registries. CONCLUSIONS: This review identified only Spine IQ as a dedicated chiropractic clinical outcomes registry, revealing a significant gap in registry infrastructure within the profession globally. The profession should explore the development of registries to enhance care quality, societal impact, and opportunities for collaborative research.
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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 | 0.092 | 0.324 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.045 | 0.052 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".