Quantitative ultrasound imaging of Achilles tendon integrity in symptomatic and asymptomatic individuals: reliability and minimal detectable change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Quantifying the integrity of the Achilles tendon (AT) is a rehabilitation challenge. Adopting quantitative ultrasound measurements (QUS measurements) of the AT could fill this gap by 1) evaluating the test-retest reliability and accuracy of QUS measurements of the AT; 2) determining the best protocol for collecting QUS measurements in clinical practice. METHODS: A total of 23 ATs with symptoms of Achilles tendinopathy and 63 asymptomatic ATs were evaluated. Eight images were recorded for each AT (2 visits × 2 evaluators × 2 images). Multiple sets of QUS measurements were taken: geometric (thickness, width, area), first-order statistics (computed from a grayscale histogram distribution: echogenicity, variance, skewness, kurtosis, entropy) and texture features (computed from co-occurrence matrices: contrast, energy, homogeneity). A generalizability study quantified the reliability and standard error of measurement (accuracy) of each QUS measurement, and a decision study identified the best measurement taking protocols. RESULTS: Geometric QUS measurements demonstrated excellent accuracy and reliability. QUS measurements computed from the grayscale histogram distribution revealed poor accuracy and reliability. QUS measurements derived from co-occurrence matrices showed variable accuracy and moderate to excellent reliability. In clinical practice, using an average of the results of three images collected by a single evaluator during a single visit is recommended. CONCLUSIONS: The use of geometric QUS measurements enables quantification of AT integrity in clinical practice and research settings. More studies on QUS measurements derived from co-occurrence matrices are warranted.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it