Classification and Diagnostic Investigation in Inflammatory Myopathies: A Study of 99 Patients
Bibliographic record
Abstract
OBJECTIVE: Insights into the pathogenesis of inflammatory myopathies have led to new diagnostic methods. The aims of our study were (1) to evaluate the consequences of using the classification of Amato/European Neuromuscular Centre Workshop (ENMC) compared to that of Bohan and Peter; and (2) to evaluate any diagnostic benefit in using an extended pathological investigation. METHODS: From a consecutive retrospective database, we evaluated 99 patients for classification. Patients with inclusion body myositis (IBM) were classified according to Griggs, et al. In addition to routine stainings and immunohistochemistry, a multilevel serial sectioning procedure was performed on paraffin-embedded material, to identify scarce pathological findings. RESULTS: Classification according to Bohan and Peter could be performed for 83 of the 99 patients, whereas only 60 patients met the Amato/ENMC criteria, the latter resulting in the following diagnostic groups: IBM (n = 18), nonspecific myositis (n = 14), polymyositis (n = 12), dermatomyositis (n = 10), dermatomyositis sine dermatitis (n = 5), and immune-mediated necrotizing myopathy (n = 1). Most of the Amato/ENMC diagnostic groups harbored patients from several of the Bohan and Peter groups, which included a substantial group lacking proximal muscle weakness. The serial sectioning procedure was essential for classification of 9 patients (15%), and led to a more specific diagnosis for 13 patients (22%) according to Amato/ENMC. CONCLUSION: The classification of Amato/ENMC was more restrictive, forming groups based on clinical criteria and specified myopathological findings, which clearly differed from the groups of the Bohan and Peter classification. An extended pathological investigation increased the diagnostic yield of a muscle biopsy and highlights the quantity and specificity of certain pathological findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".