Stress Fractures of the Calcaneus Diagnosed by Sonography: Report of 8 Cases
Bibliographic record
Abstract
We present a retrospective review of 8 patients (6 women and 2 men, with an age range of 46-80 years; mean age, 60.2 years) in whom sonography was used to diagnose a calcaneal stress fracture. Sonography was performed because of a clinical suspicion of soft tissue injuries. Two patients were first assessed by standard radiography; for the remaining patients, sonography was the first imaging technique used. Patients were subsequently examined by magnetic resonance imaging, except for 1 patient in whom the diagnosis was made only on a clinical-sonographic correlation. On sonography, there was thickening of the periosteum and subcutaneous edema in all patients; a calcified bone callus was evident in none of the 8 patients. Cortical irregularities were found in 6 of 8 patients. Color Doppler imaging showed local hypervascular changes of the periosteum in all patients. Sonography, together with clinical findings, can be used to diagnose a calcaneal stress fracture. We suggest that sonologists should include a calcaneal stress fracture in their differential diagnosis in cases of mechanical hind foot pain. They must also include, as a part of every sonographic examination of the ankle, an examination of the calcaneus and be aware of the sonographic appearance of stress fractures. If the diagnosis is still uncertain after the sonographic examination, magnetic resonance imaging should be prescribed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".