Revisiting Physical Examination: Abadie's Sign and Achilles Intratendinous Changes in Subjects with Diabetes
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate whether or not the positivity of Abadie's sign could be an indicator of asymptomatic Achilles intratendinous changes. SUBJECTS AND METHODS: A total of 18 patients (36 tendons) suffering from diabetes, with at least 1 Achilles tendon positive to Abadie's sign, were compared to matched subjects with diabetes bilaterally negative to Abadie's sign. Anthropometric measures and the Toronto Clinical Neuropathy Score were registered. Echotexture was evaluated and degenerative features classified as absent, mild, moderate and severe. The frequencies of structural abnormalities, according to both Abadie's sign and the Toronto Clinical Neuropathy Score, were determined. RESULTS: In the first group 26 out of 36 tendons (72.2%) showed positive Abadie's sign and a significantly higher frequency of moderate and severe (65.3%) structural abnormalities compared to Achilles tendons with negative sign (4.3%; p < 0.0001). This frequency was similar to that observed in the subjects with the highest Toronto Clinical Neuropathy Score (64.2%). CONCLUSIONS: This study showed that Abadie's sign was a useful tool for assisting in the diagnosis of asymptomatic Achilles intratendinous changes, which, when detected early, could help prevent unexpected tendon rupture. The concordance between Abadie's sign and Achilles sonographic abnormalities needs to be evaluated in a larger sample to consider it useful for practical purposes.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".