The Role of Sural Nerve Biopsy in the Diagnosis of Vasculitis
Bibliographic record
Abstract
OBJECTIVE: The diagnostic yield of sural nerve biopsy (SNB) in vasculitis is uncertain. Our aim was to document relevant characteristics of patients undergoing SNB in the investigation of vasculitis; determine the diagnostic yield; relate positive biopsy findings to patient demographic, laboratory, and clinical variables; and to calculate the rate of surgical complications. METHODS: Patients with suspected vasculitis that underwent SNB as part of diagnostic evaluation at academic medical centers in Sweden and the United Kingdom were identified by searching local pathology databases and clinic registers. A structured review of medical case records and pathology reports was conducted. Histological findings were categorized as definite, probable, or no vasculitis in accordance with the 2015 Brighton Collaboration reinterpretation and update of the Peripheral Nerve Society guidelines for vasculitic neuropathy. Definite and probable findings were considered positive for vasculitis. RESULTS: Ninety-one patients that underwent SNB were identified (45% female). Forty (44%) patients showed histological evidence of vasculitis: 14 definite and 26 probable. A concomitant muscle biopsy conducted in 10 patients did not contribute to the diagnostic yield. Positive antineutrophil cytoplasmic antibody test, organ involvement other than the nervous system, and a longer biopsy sample were associated with a positive biopsy. The reported surgical complication rate was 15%. CONCLUSION: SNB of sufficient length is a useful procedure to confirm a diagnosis of vasculitis.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".