Positive Outcomes Following Cervical Acceleration-Deceleration (CAD) Injury Using Chiropractic BioPhysics® Methods: A Pre-Auto Injury and Post-Auto Injury Case Series
Bibliographic record
Abstract
This series illustrates how rear-end impact motor vehicle collisions (MVCs) alter the cervical spine’s alignment and demonstrates therapeutic use of cervical extension traction to improve lordotic alignment and other outcomes. This is a retrospective reporting of 7 adult patients (4 males and 3 females, 28–42 years) treated for cervical hypolordosis. These subjects received Chiropractic BioPhysics® (CBP®) rehabilitation and then were involved in a rear-end MVC. All cases had radiographic assessment that quantified the buckling of the cervical spine, presumably resulting directly from the CAD trauma. After an average of 3 years and 9 months (range: 1–7.6 years) following their initial program of care, the 7 patients sought care for a second time after the MVC. At this time, compared with their previously recorded post-treatment spine radiographs, there was an average 18.7° (range: 7.6–35.4°) reduction in cervical lordosis, a 9.2 mm (range: 3.6–19.8 mm) increase in anterior head translation (AHT), an 11.3° (range: 0.2–19.9°) decrease in the atlas plane line (APL), as well as a 35.7% (range: 22–52%) average neck disability index score (NDI) measured after the MVC. After the crash, a second round of CBP rehabilitation was administered, resulting in an average 15.1° improvement in cervical lordosis, 10.9 mm reduction in AHT, 10.4° increase in APL, and a 23.7% drop in NDI after an average of 35 treatments over 9 weeks. Treatment was universally successful, as an average 80% re-establishment of the lordosis toward its pre-injury state was found. There were no adverse events reported. This case series demonstrates that motor vehicle collisions may alter the alignment of the cervical spine. Rehabilitation of the cervical curve using extension traction improved the patients’ initial pre-crash alignments toward their pre-injury alignments and was likely responsible for improvement in the patients’ conditions. Clinical trials are needed to confirm these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".