Use of Muscle Biopsies for Diagnosis of Systemic Vasculitides
Bibliographic record
Abstract
OBJECTIVE: Few studies have investigated the use of muscle biopsies (MB) for the diagnosis of systemic vasculitides (SV). We aimed to evaluate the diagnostic use of MB in this condition. METHODS: We reviewed 310 consecutive MB performed in our center between 2000 and 2008 and correlated them with clinical data from the corresponding patients. Thirty-one of the patients, representing a total of 33 MB, were diagnosed with active SV. MB were considered positive when they demonstrated either necrotizing vasculitis or nonnecrotizing vasculitis. RESULTS: Twenty-two of the 33 MB were positive (sensitivity of 66.7%), with necrotizing vasculitis and nonnecrotizing vasculitis being equally frequent. The SV were antineutrophil cytoplasmic antibody (ANCA)-associated in 22 patients (71%), and ANCA-negative in 9 cases (29%). Neither the type nor the clinical spectrum of the SV was predictive of MB positivity. None of the muscle symptoms (myalgias or biological rhabdomyolysis) were correlated with MB positivity. All the biopsies were performed uneventfully. CONCLUSION: The feasibility and positive predictive value of MB make it a valuable tool for ruling out a diagnosis of SV. Since no clinical signs could predict its positivity, MB should be considered in all suspected cases of SV. Unlike other biopsies, including kidney biopsy, MB had no prognostic value.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".