Are ultrasound and magnetic resonance imaging of value in assessment of Achilles tendon disorders? A two year prospective study
Bibliographic record
Abstract
OBJECTIVES: To (a) compare ultrasound (US; including grey scale and colour and power Doppler) and magnetic resonance imaging (MRI; with high resolution and fat saturation sequences) with a clinical yardstick in the evaluation of chronic Achilles tendinopathy, and (b) examine whether either imaging method predicted 12 and 24 month clinical outcome. METHODS: Forty five patients with symptoms in 57 Achilles tendons were diagnosed with tendinopathy by an experienced sports medicine doctor. All patients underwent US examination (12 MHz probe) with colour and power Doppler, and 25 consecutive patients also underwent MRI with high resolution T1 weighted and STIR sequences. RESULTS: US identified abnormal morphology in 37 of the 57 symptomatic tendons (65%) and normal morphology in 19 of 28 asymptomatic tendons (68%). Baseline US findings did not predict 12 month clinical outcome. The addition of colour and power Doppler did not improve the diagnostic performance of US. MRI identified abnormal morphology in 19 of 34 symptomatic tendons (56%) and normal morphology in 15 of 16 asymptomatic tendons (94%). Lesser grades of MR signal abnormality at baseline were associated with better clinical status at 12 month follow up. CONCLUSIONS: US and MRI show only moderate correlation with clinical assessment of chronic Achilles tendinopathy. Graded MRI appearance was associated with clinical outcome but US was not.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".