MétaCan
Menu
Back to cohort
Record W4313327656 · doi:10.3390/life13010095

Improvement of Pain and Function by Using Botulinum Toxin Type A Injection in Patients with an Osteoarthritic Knee with Patellar Malalignment: An Electromyographic Study

2022· article· en· W4313327656 on OpenAlexaboutno aff
Alice Chu Wen Tang, Chih‐Kuang Chen, Szu‐Yuan Wu, Simon F.T. Tang

Bibliographic record

VenueLife · 2022
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisBotulinum toxinAnterior knee painSurgeryPatellaPathology

Abstract

fetched live from OpenAlex

Objective: To determine the pain and electromyographic (EMG) amplitude ratio of the vastus medialis oblique (VMO) to the vastus lateralis (VL) after botulinum toxin type A (BTA) was injected in the bilateral osteoarthritic knee of patients with patellar malalignment for analysis. Material and methods: A total of fifteen patients were recruited; the more symptomatic knee of each patient received a BTA injection (BTA side). The other set of patients were left untreated. In all, fifteen healthy participants comprised the control group. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and numeric rating scale (NRS) for pain were assessed. The EMG amplitude of VL and VMO activity was recorded using an isokinetic dynamometer and synchronized using the BIOPAC MP100. The data were collected before and at 4, 8, and 12 weeks post−BTA injection. Results: The EMG ratios of the patient group were lower than those of the control group at all testing velocities (p < 0.05). The VMO/VL ratio improved significantly on the BTA side only. The VMO/VL ratios on the BTA side were higher than those on the untreated side (p < 0.05). Knee pain decreased significantly after the BTA injection. The EMG ratios were negatively correlated with the NRS and WOMAC scores. Conclusion: BTA injection effectively reduces knee pain and restores the EMG ratio between the VMO and VL.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.178
Teacher spread0.171 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueLifeSame topicLower Extremity Biomechanics and PathologiesFrench-language works237,207