Short-Term Usual Chiropractic Care for Spinal Pain
Bibliographic record
Abstract
In Brief Study Design. Parallel-group randomized controlled trial. Objective. Establish the short-term effectiveness of chiropractic therapy for spinal pain compared with a sham intervention and explore the predictors of chiropractic treatment satisfaction. Summary of Background Data. Chiropractic treatment is widely used for spinal pain. However, a lack of sound evidence precludes conclusions about the effectiveness of chiropractic for spinal pain. Methods. Participants were adults experiencing spinal pain, randomized to receive 2 treatments of chiropractic or sham therapy. Participants and outcome assessors were blinded to group allocation. Primary outcomes at 2 weeks were pain intensity (0–10 scale) and function (0–40 Functional Rating Index). Secondary outcomes were global change, minimum acceptable outcome, and treatment satisfaction. Treatment effects were estimated with linear mixed models for the primary outcomes. We used logistic regression to identify differences in the secondary outcomes and explore for predictors of treatment satisfaction. Results. One hundred eighty three participants (chiropractic, n = 92; sham, n = 91) were recruited and included in the analyses. Participants receiving chiropractic therapy reported greater improvements in pain (mean difference, 95% confidence interval [CI] = 0.5 [0.1–0.9]), physical function (mean difference [95% CI] = 2.1 [0.3–4.0]), and were more likely to experience global improvement (48% vs. 24%, P = 0.01) and treatment satisfaction (78% vs. 56%, P < 0.01). There was no between-group difference in achieving a minimally acceptable outcome (34% sham vs. 29% chiropractic, P = 0.42). Awareness of treatment assignment and achieving minimally important improvement in pain intensity were associated with chiropractic treatment satisfaction. Conclusion. Short-term chiropractic treatment was superior to sham; however, treatment effects were not clinically important. Awareness of treatment assignment and clinically important reductions in pain were associated with chiropractic treatment satisfaction. Level of Evidence: 2 We examined whether 2 treatments of usual chiropractic care are more effective than a sham intervention for spinal pain in the short term. Differences between groups for pain and function were statistically significant but not clinically important. Future research should identify diagnostic subsets to which chiropractic treatments are best suited.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".