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Record W4224311163 · doi:10.1123/jsr.2021-0254

Effectiveness of Ultrasound-Guided Corticosteroid Injections, Prolotherapy, and Exercise Therapy on Partial-Thickness Supraspinatus Tears

2022· article· en· W4224311163 on OpenAlexaboutno aff
Ali Eroğlu, Melda Pelin Yargıç

Bibliographic record

VenueJournal of Sport Rehabilitation · 2022
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsProlotherapyMedicineTearsRotator cuffUltrasoundPhysical therapyCorticosteroidSupraspinatus muscleSurgeryRadiologyPathology

Abstract

fetched live from OpenAlex

CONTEXT: To investigate the effects of steroid injection (STE), prolotherapy (PRO), and exercise therapy in the treatment of partial tears of the supraspinatus. DESIGN: A retrospective cohort study. METHODS: A total of 64 patients with clinically and radiologically diagnosed partial-thickness supraspinatus tear who received either a cortisone injection (STE), dextrose PRO, or physical therapy combined with home-based exercise therapy were included. Main outcome measures were patients' visual analog scale scores, Western Ontario Rotator Cuff (WORC) Index scores, and the Shoulder Pain and Disability Index scores at the baseline, 3 weeks, and 3 months. RESULTS: The effect of group, time, and group-time interaction on visual analog scale, WORC, and Shoulder Pain and Disability Index scores was statistically significant (P < .001). Visual analog scale and Shoulder Pain and Disability Index scores were the lowest in the STE group at week 3, and the lowest in the PRO group at month 3 (P < .001). WORC scores of the STE group were the highest at week 3 (P < .001). At month 3, WORC scores of STE and PRO groups were similar (P = .089), but significantly higher than exercise therapy. CONCLUSIONS: Corticosteroids provide a fast pain-relieving effect and improvement in function in partial-thickness rotator cuff tears, but these effects diminish over time, whereas PRO provides a long-lasting effect.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.311
Teacher spread0.296 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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