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Record W2263276375 · doi:10.3810/psm.2000.05.890

Overuse Tendinosis, Not Tendinitis

2000· article· en· W2263276375 on OpenAlexaff
Karim Khan, Jill Cook, Jack Taunton, Fiona Bonar

Bibliographic record

VenueThe Physician and Sportsmedicine · 2000
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTendinosisTendinitisMedicineRotator cuffTendinopathyTennis elbowPhysical therapyEpicondylitisElbowChiropracticPhysical medicine and rehabilitationTendonitisTendonSurgeryPathologyAlternative medicine

Abstract

fetched live from OpenAlex

Overuse tendinopathies are common in primary care. Numerous investigators worldwide have shown that the pathology underlying these conditions is tendinosis or collagen degeneration. This applies equally in the Achilles, patellar, medial and lateral elbow, and rotator cuff tendons. If physicians acknowledge that overuse tendinopathies are due to tendinosis, as distinct from tendinitis, they must modify patient management in at least eight areas. These include adaptation of advice given when counseling, interaction with the physical therapist and athletic trainer, interpretation of imaging, choice of conservative management, and consideration of whether surgery is an option.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.017
GPT teacher head0.281
Teacher spread0.264 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations230
Published2000
Admission routes1
Has abstractyes

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