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Record W2995261749 · doi:10.1016/j.jmpt.2018.11.025

Reflex Responses of Neck, Back, and Limb Muscles to High-Velocity, Low-Amplitude Manual Cervical and Upper Thoracic Spinal Manipulation of Asymptomatic Individuals—A Descriptive Study

2019· article· en· W2995261749 on OpenAlexaff
Lindsay M. Gorrell, Philip J. Conway, Walter Herzog

Bibliographic record

VenueJournal of Manipulative and Physiological Therapeutics · 2019
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Calgary
FundersFoundation for Chiropractic Education and Research
KeywordsMedicineAsymptomaticReflexElectromyographyUpper limbCervical spineAnatomySpinal manipulationPhysical medicine and rehabilitationCervical vertebraeChiropracticSurgeryAnesthesia

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this research was to determine the extent of reflex responses after spinal manipulative therapy (SMT) of the cervical and upper thoracic spine. METHODS: Eleven asymptomatic participants received 6 commonly used SMTs to the cervical and upper thoracic spine. Bipolar surface electromyography electrodes were used to measure reflex responses of 16 neck, back, and proximal limb muscles bilaterally. The percentage of occurrence and the extent of reflex responses of these muscles were determined. RESULTS: Reflex responses after cervical SMT were typically present in all neck and most back muscles, whereas responses in the outlets to the arm and leg were less frequent. This trend was similar, although decreased in magnitude, after thoracic SMT. CONCLUSION: Reflex responses were greatest after upper cervical SMT and lowest with thoracic SMT.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.176
GPT teacher head0.399
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2019
Admission routes1
Has abstractyes

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