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Record W3025742874

Tendon neuroplastic training for lateral elbow tendinopathy: 2 case reports.

2018· article· en· W3025742874 on OpenAlexaff
Patrick Welsh

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsCanadian Memorial Chiropractic College
Fundersnot available
KeywordsMedicineTendinopathyPhysical medicine and rehabilitationPhysical therapyNeuroplasticityElbowTendonRehabilitationEccentric trainingMetronomeChronic painSurgeryEccentricInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To report 2 cases of lateral elbow tendinopathy treated with a novel adaptation of tendon neuroplastic training (TNT). CLINICAL FEATURES: A 37-year-old male electrician presented with two months of recurrent left lateral elbow pain related to repetitive motions of gripping and pulling at work. INTERVENTION AND OUTCOME: Both patients underwent 8 weeks of a novel rehabilitation program, including TNT, which involved pacing their resistance exercises to a metronome. Both patients experienced clinically meaningful improvements in pain and functional outcome scores that were sustained at the 3-month follow-up. SUMMARY: Recent evidence suggests that the central nervous system may play a role in chronic tendinopathies. It is possible that TNT may address the central nervous system component of chronic/recurrent tendinopathy that is not addressed by traditional passive therapies. However, further research is needed.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0080.003
Insufficient payload (model declined to judge)0.0050.002

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 head0.047
GPT teacher head0.269
Teacher spread0.222 · 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 designCase report
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

Citations12
Published2018
Admission routes1
Has abstractyes

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