Quantitative ultrasound imaging of Achilles tendon integrity in symptomatic and asymptomatic individuals: reliability and minimal detectable change
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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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 | 0.024 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".