Reliability of ultrasound evaluation of the long head of the biceps tendon
Bibliographic record
Abstract
OBJECTIVE: To determine the reliability of quantitative measures of the long head of the biceps tendon using an ultrasound-imaging system. DESIGN: Intra- and inter-rater reliability study. SUBJECTS/PATIENTS: Thirty-one participants without shoulder pain. METHODS: All participants took part in 3 ultrasound imaging sessions; they were assessed by 2 evaluators (inter-rater reliability), one of whom assessed them twice (intra-rater reliability). All measurements were taken at the widest identified part of the tendon using longitudinal and transverse views. Measurements of the long head of the biceps tendon included width, thickness and cross-sectional area. Intraclass correlation coefficients and minimal detectable change were used to characterize reliability. RESULTS: Intra- and inter-rater reliabilities were excellent for all measures when the mean of 2 measures were considered, except for inter-rater reliability of the width, for which it ranged from 0.76 to 0.86. Minimal detectable change ranged from 0.3 to 1.6 mm for width and thickness, and from 2.8 to 4.9 mm2 for cross-sectional area. CONCLUSION: Ultrasound measurement of the long head of the biceps tendon is a highly reliable method, except for the width. When measuring the long head of the biceps tendon, a mean of 2 measurements is recommended. Now that reliability has been shown in healthy individuals, the next step will be to determine the validity/reliability of these quantitative measures in symptomatic shoulders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".