Radiological identification and analysis of soft tissue musculoskeletal calcifications
Bibliographic record
Abstract
Musculoskeletal calcifications are frequent on radiographs and sometimes problematic. The goal of this article is to help radiologists to make the correct diagnosis when faced with an extraosseous musculoskeletal calcification. One should first differentiate a calcification from an ossification or a foreign body and then locate the calcification correctly. Each location has a specific short differential diagnosis, with minimal further investigation necessary. Intra-tendon calcifications are most frequently associated with hydroxyapatite deposition disease (HADD). In most cases, intra-articular calcifications are caused by calcium pyrophosphate dihydrate (CPPD) crystal deposition disease. Soft tissue calcification can be caused by secondary tumoural calcinosis from renal insufficiency, or collagen vascular diseases and by vascular calcifications, either arterial or venous (phlebolith). TEACHING POINTS: • Calcifications have to be differentiated form ossification and foreign body. • A musculoskeletal MRI study must always be correlated with a radiograph. • The clinical manifestations of calcifications may sometimes mimic septic arthritis or sarcoma. • HADD and CPPD crystal deposition have a distinct appearance on radiograph. • Calcinosis is more frequently caused by chronic renal failure and scleroderma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".