P01.01. Neural responses to the mechanical characteristics of a spinal manipulation: effect of varying segmental contact site
Bibliographic record
Abstract
In an anesthetized cat preparation, a simulated spinal manipulation [posterior-to-anterior; thrust amplitude = 21.3N (55% of an average cat’s body weight of 3.95 kg); thrust duration = 100ms] was delivered to the intact lower lumbar spine (L 6 – S 1) at each of 4 contact sites: L 6 spinous process, left L 6 mammillary process, left L 6 lamina, and L 7 spinous process. Electrophysiological recordings from individual muscle spindle afferents (n=16) innervating the L 6 multifidus and longissimus muscles were obtained from L 6 dorsal rootlets exposed through an L 5 laminectomy. Changes in neural activity during the manipulative thrust were compared between the four contact sites. All contact sites increased mean spindle activity: L 6 spinous: 85 impulses per second (imp/s) (60, 100; lower, upper 95% CI); L 6 lamina: 104 imp/s (79, 130); L 6 mammillary 80 imp/s (55, 105); and L 7 spinous 43 imp/s (18, 68). Lamina contact produced the largest increase, but only differences between the L 7 spinous and each L 6 contact site were statistically significant. The data suggest that maximizing sensory input from segmental paraspinal tissues during a spinal manipulation requires specifically contacting that segmental level. In addition, a lamina contact may most effectively create the dynamic mechanical stimulus that evokes the sensory input.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.094 | 0.010 |
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".