Neck pain and disability outcomes following chiropractic upper cervical care: a retrospective case series.
Bibliographic record
Abstract
OBJECTIVE: To investigate the use of an upper cervical low-force (UCLF) chiropractic procedure, based on a vertebral alignment model, in the management of neck pain and disability by assessing the impact on valid patient outcome measures. DESIGN: A retrospective case series. METHODS: Consecutive patient files at a private chiropractic practice over a 1-year period were reviewed for inclusion. Data for the first visit, pre- and post-adjustment atlas alignment radiographic measurements, baseline and 2-weeks NDI (100 point) and verbal NRS (11 point) were recorded. The data were analyzed in their entirety and by groups comparing <30% vs. >30% post adjustment atlas alignment changes. RESULTS: Statistically significant clinically meaningful improvements in neck pain NRS (P < 0.01) and disability NDI (P < 0.01) after an average of 13.6 days of specific chiropractic care including 5.7 office visits and 2.7 upper cervical adjustments were demonstrated. There were no serious adverse events. Cases with the post-adjustment skull/atlas alignment measurement (atlas laterality) that were changed more than 30% on the first visit toward the orthogonal alignment predicted a statistically and clinically significant better outcome for NDI in 2 weeks. CONCLUSIONS: UCLF chiropractic instrument adjustments utilizing a vertebral alignment model are promising for the management of patients with neck pain based on assessment using valid outcome measures.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".