Increased prevalence of autoimmune diseases in children with chronic spontaneous urticaria
Bibliographic record
Abstract
Increased prevalence of Autoimmune Diseases in Children with Chronic Spontaneous UrticariaMichelle Le, MD1, Lydia Zhang MD2, Sofianne Gabrielli MSc2, Connor Prosty BSc, MD(c)1, Laura May Miles LLM, MD(c)2, Elena Netchiporouk, MD, MSc1, Sharon Baum, MD3, Shoshana Greenberger, MD3, Luis F. Ensina, MD, MSc, PhD4, Fatemeh Jafarian, MD1, Xun Zhang, PhD5, Moshe Ben-Shoshan, MD, MSc21Division of Dermatology, McGill University, Montreal, QC, Canada2Division of Pediatric Allergy and Clinical Immunology, Department of Pediatrics, McGill University Health Centre, Montreal, QC, Canada3Department of Dermatology, Chaim Sheba Medical Center, Tel-Aviv University, Sackler School of Medicine, Tel Hashomer, Israel4Department of Pediatrics, Federal University of São Paolo, Brazil5Centre for Outcome Research and Evaluation, Research Institute of McGill University Health Centre, Montreal, QC, CanadaCorrespondence: Michelle Le, M.D., 1001 Decarie Blvd, Montréal, Québec, H4A 3J1 email: michelle.le@mail.mcgill.caArticle type: LetterManuscript word count: 981References : 10Tables : 1Figures: NoneFunding sources : NoneConflicts of interest: None declaredIRB Approval Status : Reviewed and approved by McGill University Health Centre; approval 12-255GEN
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".