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EFFECTIVENESS OF CHIROPRACTIC ADJUSTMENT IN LUMBAR PAIN IN CROSSFIT PRACTITIONERS

2017· article· en· W2946488694 on OpenAlexaboutno aff
Desiree Moehlecke, Luiz Alberto Forgiarini

Bibliographic record

VenueColuna/Columna · 2017
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsChiropracticPhysical therapyMedicineRange of motionVisual analogue scaleMassageLow back painMcGill Pain QuestionnaireLumbarManual therapyRandomized controlled trialJoint mobilizationPhysical medicine and rehabilitationBack painAlternative medicineSurgery

Abstract

fetched live from OpenAlex

ABSTRACT Objective: To evaluate the efficacy of acute chiropractic adjustment in individuals who practice CrossFit with regard to complaints of low back pain and the joint range of motion in this region. Methods: A randomized clinical trial comprised of CrossFit practitioners from a box in Novo Hamburgo-RS, of both sexes and aged 18 to 40 years who had low back pain at the time of the study. The following tools were used: Semi-structured Anamnesis Questionnaire, Visual Analog Scale, McGill Pain Questionnaire, and SF-36 Quality of Life Questionnaire. Individuals in the control group answered the questionnaires before and after CrossFit training. The chiropractic group performed the same procedure, plus pre-training chiropractic adjustment and joint range of motion (ROM) before and after lumbar adjustment. Results: There was a significant increase in pain in the control group, and a significant decrease in pain in the chiropractic group, including one day after the chiropractic adjustment. In the chiropractic group, the joint ranges of motion had a significant increase in flexion and extension of the lumbar spine after chiropractic adjustment. Conclusion: The chiropractic group achieved a significant improvement in pain level and joint range of motion, suggesting that acute chiropractic adjustment was effective in reducing low back pain.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.173
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
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 head0.015
GPT teacher head0.325
Teacher spread0.310 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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