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Record W4210730680 · doi:10.3389/fnint.2021.809372

Force Distribution Within Spinal Tissues During Posterior to Anterior Spinal Manipulative Therapy: A Secondary Analysis

2022· article· en· W4210730680 on OpenAlexafffund
Martha Funabashi, Alexander Breen, Diana De Carvalho, Isabelle Pagé, François Nougarou, Martin Descarreaux, Gregory N. Kawchuk

Bibliographic record

VenueFrontiers in Integrative Neuroscience · 2022
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsUniversity of AlbertaMemorial University of NewfoundlandUniversité du Québec à Trois-RivièresCanadian Memorial Chiropractic College
FundersCanadian Institutes of Health ResearchChildren's Hospital FoundationStollery Children’s Hospital FoundationAlberta Innovates - Health SolutionsWomen and Children's Health Research InstituteChildren's Health Research Institute
KeywordsKinematicsBiomechanicsBiomedical engineeringAnatomyMaterials scienceMedicinePhysics

Abstract

fetched live from OpenAlex

BACKGROUND: Previous studies observed that the intervertebral disc experiences the greatest forces during spinal manipulative therapy (SMT) and that the distribution of forces among spinal tissues changes as a function of the SMT parameters. However, contextualized SMT forces, relative to the ones applied to and experienced by the whole functional spinal unit, is needed to understand SMT's underlying mechanisms. AIM: To describe the percentage force distribution between spinal tissues relative to the applied SMT forces and total force experienced by the functional unit. METHODS: This secondary analysis combined data from 35 fresh porcine cadavers exposed to a simulated 300N SMT to the skin overlying the L3/L4 facet joint via servo-controlled linear motor actuator. Vertebral kinematics were tracked optically using indwelling bone pins. The functional spinal unit was then removed and mounted on a parallel robotic platform equipped with a 6-axis load cell. The kinematics of the spine during SMT were replayed by the robotic platform. By using serial dissection, peak and mean forces induced by the simulated SMT experienced by spinal structures in all three axes of motion were recorded. Forces experienced by spinal structures were analyzed descriptively and the resultant force magnitude was calculated. RESULTS: During SMT, the functional spinal unit experienced a median peak resultant force of 36.4N (IQR: 14.1N) and a mean resultant force of 25.4N (IQR: 11.9N). Peak resultant force experienced by the spinal segment corresponded to 12.1% of the total applied SMT force (300N). When the resultant force experienced by the functional spinal unit was considered to be 100%, the supra and interspinous ligaments experienced 0.3% of the peak forces and 0.5% of the mean forces. Facet joints and ligamentum flavum experienced 0.7% of the peak forces and 3% of the mean forces. Intervertebral disc and longitudinal ligaments experienced 99% of the peak and 96.5% of the mean forces. CONCLUSION: In this animal model, a small percentage of the forces applied during a posterior-to-anterior SMT reached spinal structures in the lumbar spine. Most SMT forces (over 96%) are experienced by the intervertebral disc. This study provides a novel perspective on SMT force distribution within spinal tissues.

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)0.000
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 score0.440
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.306
Teacher spread0.284 · 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

Citations11
Published2022
Admission routes2
Has abstractyes

Explore more

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