Assessing joint vibration during spinal manipulation
Bibliographic record
Abstract
This IRB‐approved study assessed the relationship between cavitation and zygapophysial (Z) joint gapping following spinal manipulation (adjusting) in 5 healthy volunteers. High signal MRI markers were used to accurately identify the T12, L3, and S1 spinous processes during scout views. Axial images of the L4/L5 and L5/S1 levels were obtained in the neutral supine position. Each subject was then positioned on the side, and accelerometers were placed over the marked spinous processes. Recording from the accelerometers was done from final positioning through adjusting. The subject was then scanned in side posture position (accelerometers removed). Using a digitizer, the greatest A‐P central Z joint articular surface distances were measured on the 1 st and 2 nd scans; the difference between the 2 measurement values was the gapping difference, GD; a positive value indicating an increase in gapping following the spinal adjustment. GD was compared between up‐side (adjusted), down‐side (non‐adjusted), cavitation, and non‐cavitation joints. Results GD was increased in Z joints that were adjusted [0.5 (SE 0.2) mm] vs. non‐adjusted [−0.2 (SE 0.2) mm], and vertebral segments with cavitation gapped more than no cavitation [0.8 (SE 0.4) vs. 0.4 (SE 0.2) mm]. Conclusions A future clinical study is quite feasible. Forty subjects would be needed for appropriate power (0.80). Funding: NIH/NCCAM (#2R01AT000123).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".