Real-Time Sonoelastography: Findings in Patients with Symptomatic Achilles Tendons and Comparison to Healthy Volunteers
Bibliographic record
Abstract
PURPOSE: Real-time sonoelastography (SE), a newly introduced ultrasound technique, has already shown conclusive results in breast, prostate, and thyroid tumor diagnostics. This study investigated the performance of SE for the differentiation of Achilles tendon alterations of tendinopathy compared to clinical examination and conventional ultrasound (US). MATERIALS AND METHODS: Achilles tendons in 25 consecutive patients with chronic Achilles tendinopathy and 25 healthy volunteers were examined clinically by US and by SE. RESULTS: In the healthy volunteers, SE showed the tendon to be hard (93 %), while distinct softening was found in 57 % of the patients. SE showed more frequent involvement of the distal (64 %) and middle third (80 %) than the proximal third (28 %) of the Achilles tendon. Using SE a mean sensitivity of 94 %, specificity of 99 %, and accuracy of 97 % were found when clinical examination was used as the reference standard. The correlation to US was 0.89. Mild softening was found in 7 % of the healthy volunteers and in 11 % of the patients. CONCLUSION: Our results emphasize that only distinct softening of Achilles tendons is comparable to clinical examination and US findings. However, mild softening might be explained by very early changes in tissue elasticity in the case of Achilles tendinopathy, which should be assessed in follow-up studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".