Repeatability of ultrasound in assessment of distal biceps brachii tendon
Bibliographic record
Abstract
Objective: Examine the repeatability of ultrasound imaging for capturing cross sectional area (CSA), tendon length and elongation of the distal biceps brachii (dBB) tendon at rest and during submaximal elbow flexion contractions. The secondary purpose was to assess the influence of these architectural measures on tendon mechanics of stress and strain.Methods: Within a testing session and between two days CSA, tendon length and elongation of the dBB were captured with ultrasound. Measures were compared within a session and between days. Pearson’s correlations were performed to determine the intra-class correlation coefficients. Bland and Altman plots were used to identify the agreement between measures as well as the bias in measurements. Paired T-test were performed to ensure the calculated variables did not differ between days. Results: Resting tendon CSA was repeatable and strongly correlated (r = 0.98) within a session and between days; however, modest differences were observed in resting tendon length between days (1.8 mm) although values were correlated (r = 0.98). During submaximal contractions of 10%-80% maximal tendon elongation (r = 0.83) and CSA (r = 0.98) were also repeatable. From the measures of elongation and CSA, the calculated values of tendon strain (r = 0.97) and stress (r = 0.96) were also repeatable.Conclusions: Elongation and CSA of the dBB tendon captured with ultrasound are repeatable between testing sessions. Fromthese measures tendon mechanics can be calculated to define the tendon’s role in upper limb tasks, long-term adaptation anddiagnostics.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".