Dr. Kitajima et al reply
Bibliographic record
Abstract
To the Editor: We thank Mutoh et al1 for their interest in our study on antimelanoma differentiation-associated gene 5 antibody–positive interstitial lung disease (anti-MDA5-ILD) after vaccination with coronavirus disease 2019 (COVID-19) mRNA vaccines2 and for sharing their clinical experience. Mutoh et al1 reported a case of anti-MDA5-ILD that developed 8 weeks after COVID-19 mRNA vaccination in Japan. This case is similar to the cases that we have encountered. A recent literature review also reported 7 cases of anti-MDA5-ILD after COVID-19 vaccination.3 Collectively, these cases provide evidence of an association between vaccination with COVID-19 mRNA vaccines and anti-MDA5-ILD, and suggest the possibility of COVID-19 mRNA-vaccine-induced anti-MDA5-ILD. However, these cases do … Address correspondence to Dr. T. Kitajima, Respiratory Disease Center, Tazuke Kofukai Medical Research Institute, Kitano Hospital, 2-4-20 Ohgimachi, Kita-ku, Osaka 530-8480, Japan. Email: m-kitajima{at}kita-no-hp.or.jp.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.027 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".