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Record W4417333396 · doi:10.4103/jpbs.jpbs_1527_25

Comparative Analysis of Ultrasound and MRI in Diagnosing Rotator Cuff Injuries in a Tertiary Care Setting

2025· article· en· W4417333396 on OpenAlexaff
Sudhakar Pandya, Poonam Dabhade, Harsh Anadkat, Vivek Amritbhai Patel, Kanval Shah, Nikhil Vaidya

Bibliographic record

VenueJournal of Pharmacy And Bioallied Sciences · 2025
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsASTER
Fundersnot available
KeywordsRotator cuffUltrasoundBicepsMagnetic resonance imagingTearsUltrasonographyTendonDiagnostic accuracy

Abstract

fetched live from OpenAlex

A BSTRACT Aim: The aim of this study is to assess the diagnostic accuracy of ultrasonography in comparison to magnetic resonance imaging for evaluating rotator cuff injuries, while also examining the reliability of ultrasonography as a primary imaging technique. Materials and Methods: A prospective observational study of 84 individuals focused on those with shoulder pain indicating rotator cuff disease. After an ultrasound, all patients had an MRI. Sensitivity, specificity, PPV, NPV, and diagnostic accuracy were calculated for tendon and bursal structures. Results: The findings indicate that ultrasound exhibited the highest sensitivity for identifying supraspinatus tears at 83.85%. Additionally, it showed a notable specificity in assessing subscapularis (94.74%), biceps tendon (96.65%), and bursitis-related conditions. A statistically significant correlation was observed in the evaluation of the subscapularis using MRI, with a P value of 0.048. Conclusion: The high specificity and accessibility of this imaging tool underscore its importance as a primary option, while MRI continues to play a crucial role in addressing complex or posterior shoulder pathologies.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.027
GPT teacher head0.396
Teacher spread0.369 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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