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Record W4239125923 · doi:10.1186/1472-6882-12-s1-p1

P01.01. Neural responses to the mechanical characteristics of a spinal manipulation: effect of varying segmental contact site

2012· article· en· W4239125923 on OpenAlexaff
Joel G. Pickar, William Reed, Cynthia R. Long, Greg Kawchuk

Bibliographic record

VenueBMC Complementary and Alternative Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicinePhysical medicine and rehabilitationNeuroscience

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.359
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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