Distribution of Cavitations as Identified with Accelerometry During Lumbar Spinal Manipulation
Bibliographic record
Abstract
OBJECTIVE: This project determined the location and distribution of cavitations (producing vibrations and audible sounds) in the lumbar zygapophyseal (Z) joints that were targeted by spinal manipulative therapy (SMT). METHODS: This randomized, controlled, clinical study assessed 40 healthy subjects (20 men, 20 women) 18 to 30 years of age who were block randomized into SMT (group 1, n = 30) or side-posture positioning only (group 2; control, n = 10) groups. Nine accelerometers were placed on each patient (7 on spinous processes/sacral tubercles of L1-S2 and 2 placed 3 cm left and right lateral to the L4/L5 interspinous space). Accelerometer recordings were made during side-posture positioning (groups 1 and 2) and SMT (group 1 only). The SMT was delivered by a chiropractic physician with 19 years of practice experience and included 2 high-velocity, low-amplitude thrusts delivered in rapid succession. Comparisons using χ(2) or McNemar test were made between number of joints cavitating from group 1 vs group 2, upside (contact side for SMT) vs downside, and Z joints within the target area (L3/L4, L4L5, L5/S1) vs outside the target area (L1/L2, L2/L3, sacroiliac). RESULTS: Fifty-six cavitations were recorded from 46 joints of 40 subjects. Eight joints cavitated more than once. Group 1 joints cavitated more than group 2 joints (P < .0001), upside joints cavitated more than downside joints (P < .0001), and joints inside the target area cavitated more than those outside the target area (P < .01). CONCLUSIONS: Most cavitations (93.5%) occurred on the upside of SMT subjects in segments within the target area (71.7%). As expected, SMT subjects cavitated more frequently than did subjects with side-posture positioning only (96.7% vs 30%). Multiple cavitations from the same Z joints had not been previously reported.
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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) | 0.000 | 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 | 0.000 | 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".