Sound and vibration recordings during normal motion and spinal manipulation
Bibliographic record
Abstract
Novel methods assessed and localized zygapophyseal (Z) joint audible sounds (crepitus and cavitations) during normal lumbar motion and side‐posture spinal manipulation (SMT). Methods Five healthy subjects ages 21–65 years had 9 accelerometers and one specialized directional microphone (with modifications) applied to their lumbar region in a previously developed pattern that allowed specific localization of joint sounds. Each subject underwent full lumbar ranges of motion (ROM), lumbar SMT (left side = up‐side), and repeated full ROM; all while recordings were made from the accelerometers and microphone. Accelerometer and microphone data were assessed for cavitations and crepitus. Results Vibration and acoustic methods were successfully implemented and provided complementary information that identified cavitations, crepitus, and verified artifacts. Ten cavitations and 1 crepitus were recorded from 7 joints (all left side). Three instances of multiple joint cavitations were identified [2 from 2 joints (L L1/L2 and L L3/L4) and 3 from 1 joint (L L1/L2)]. Some recordings identified a cavitation when inaudible to subject and clinician. Conclusions Accelerometers and microphone provided unique and complementary information. Assessing accelerometer and acoustic data could help deepen understanding of spinal mechanics and SMT. Funding: NIH/NCCAM Grant # 3R01AT000123‐06S2.
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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 | 0.000 | 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) | 0.002 | 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".