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Record W4407111039 · doi:10.7752/jpes.2024.11298

CAPACITIVE AND RESISTIVE ELECTRICAL TRANSFER METHOD FOR ATHLETES WITH NON-SPECIFIC LOW BACK PAIN

2024· paratext· W4407111039 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Physical Education and Sport · 2024
Typeparatext
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics

Abstract

fetched live from OpenAlex

The popularity of capacitive and resistive electrical transfer (TECAR) therapy has increased over the last twenty years, with research highlighting its effectiveness for various musculoskeletal injuries and chronic pain conditions.Purpose: This study aims to evaluate the clinical efficacy of TECAR therapy as an adjunct to the rehabilitation program for athletes suffering from non-specific chronic low back pain (LBP).Materials and methods: The study tracked changes in pain (using the modified Merle d'Aubigne pain scale), mobility (assessed by the Schober test), muscle strength (measured by Kiel's test), and functionality (evaluated with the Roland-Morris questionnaire and the Quebec scale).Results: Measurements and tests were performed before and after the therapeutic course.The data indicate improved outcomes in pain reduction, soft tissue mobility, muscle endurance, and lumbar spine functionality for patients in the experimental group (Mann-Whitney nonparametric test).Statistically significant differences were observed across all indicators favoring patients treated with TECAR, except for back muscle endurance as assessed by Kiel's test.Conclusions: TECAR therapy demonstrates superior clinical outcomes in pain relief, mobility, and lumbar spine functionality, making it a valuable addition to specialized kinesitherapy programs for athletes with non-specific LBP.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.013
GPT teacher head0.310
Teacher spread0.297 · 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.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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