Long-term stability of reducing cervical kyphosis via Chiropractic Biophysics<sup>®</sup> extension traction procedures: a case series
Bibliographic record
Abstract
[Purpose] To present a case series of five patients who presented with a cervical kyphosis and chronic neck pain who were treated with Chiropractic Biophysics® (CBP®) extension traction as part of a multimodal program. [Participants and Methods] Five patients with cervical kyphosis and chronic neck pain were randomly selected from files from one clinic. All patients refrained from follow-up treatments after the initial trial of corrective care of CBP used to improve the cervical lordosis. Treatment included extension traction to the neck as well as mirror image® extension exercises and spinal manipulative therapy. The patients were treated from 2–4 months and follow-up assessment was performed at least 1 year later. [Results] After treatment the patients demonstrated an average increase in global lordosis of 24° and a decrease in the regional cervical kyphosis of 18°. The patients experienced a 5-point improved pain intensity and 24% improved disability. Follow-up of over a year demonstrated a 10° loss of original lordosis correction but no change in disability. [Conclusion] In this randomly selected series, CBP rehabilitation protocols were successful at reducing gross cervical kyphosis, however, a regression in correction occurred supporting the need for further maintenance treatments required to stabilize the original correction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".