Differentiation Between Wegener’s Granulomatosis and Microscopic Polyangiitis by an Artificial Neural Network and by Traditional Methods
Bibliographic record
Abstract
OBJECTIVE: To investigate the operating characteristics of the American College of Rheumatology (ACR) traditional format criteria for Wegener's granulomatosis (WG), the Sørensen criteria for WG and microscopic polyangiitis (MPA), and the Chapel Hill nomenclature for WG and MPA. Further, to develop and validate improved criteria for distinguishing WG from MPA by an artificial neural network (ANN) and by traditional approaches [classification tree (CT), logistic regression (LR)]. METHODS: All criteria were applied to 240 patients with WG and 78 patients with MPA recruited by a multicenter study. To generate new classification criteria (ANN, CT, LR), 23 clinical measurements were assessed. Validation was performed by applying the same approaches to an independent monocenter cohort of 46 patients with WG and 21 patients with MPA. RESULTS: A total of 70.8% of the patients with WG and 7.7% of the patients with MPA from the multicenter cohort fulfilled the ACR criteria for WG (accuracy 76.1%). The accuracy of the Chapel Hill criteria for WG and MPA was only 35.0% and 55.3% (Sørensen criteria: 67.2% and 92.4%). In contrast, the ANN and CT achieved an accuracy of 94.3%, based on 4 measurements (involvement of nose, sinus, ear, and pulmonary nodules), all associated with WG. LR led to an accuracy of 92.8%. Inclusion of antineutrophil cytoplasmic antibodies did not improve the allocation. Validation of methods resulted in accuracy of 91.0% (ANN and CT) and 88.1% (LR). CONCLUSION: The ACR, Sørensen, and Chapel Hill criteria did not reliably separate WG from MPA. In contrast, an appropriately trained ANN and a CT differentiated between these disorders and performed better than LR.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".