Does the BNT162b2 Vaccine Trigger Antimelanoma Differentiation-Associated Gene 5 Antibody–Positive Interstitial Lung Disease?
Bibliographic record
Abstract
To the Editor: We read the report by Kitajima et al1 with great interest. They demonstrated 4 cases of antimelanoma differentiation-associated gene 5 (anti-MDA5)–positive interstitial lung disease (ILD) manifested following SARS-CoV-2 vaccination and suggested the possibility of the increased incidence of anti-MDA5-associated ILD (anti-MDA5-ILD) due to the vaccination. This study interested us since we also encountered a case of new-onset anti-MDA5 antibody–positive clinically amyopathic dermatomyositis (CADM) with ILD, developed 8 weeks after BNT162b2 vaccination during the SARS-CoV-2 vaccination campaign in Japan. Herein, we briefly describe the case and address several issues to advance basic and clinical research in ILD related to anti-MDA5 antibodies. Written informed consent for publication was obtained from the patient, and the study design was approved by the appropriate ethics review board; ethics approval was not required. A 39-year-old Japanese woman with no significant medical background was referred to our hospital with a 2-month history of gradually deteriorating polyarthralgia and erythema on her fingers, and a 1-month history of face erythema, all of … Address correspondence to Dr. T. Mutoh, Department of Rheumatology, Osaki Citizen Hospital, 3-8-1 Furukawa Honami, Osaki, Miyagi 989-6183, Japan. Email: qma-kyo{at}hotmail.co.jp.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".