COVID-19 in Patients With Connective Tissue Disease-related Interstitial Lung Disease
Bibliographic record
Abstract
To the Editor: Infection with the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifests in a myriad of ways, ranging from asymptomatic disease to pneumonia and acute respiratory distress syndrome. Advanced age and underlying cardiovascular/pulmonary conditions appear to increase the risk for COVID-19 complications1,2. Patients with connective tissue disease–related interstitial lung disease (CTD-ILD) may represent a vulnerable patient population for COVID-19 given their diminished pulmonary reserve. To our knowledge, there are no prospective data reporting outcomes of SARS-CoV-2 infection in patients with CTD-ILD. We herein present all known cases (n = 4) of COVID-19 in patients with CTD-ILD at the University of California Los Angeles (UCLA) between January 2020 and August 2020. Ethics approval was not obtained for this study. The UCLA Institutional Review Board does not consider cases studies to be research as defined by federal regulations. We obtained all patients’ written informed consent to publish the material. Case 1: Systemic sclerosis–related ILD . EL is a 55-year-old Latina female with pulmonary hypertension and progressive systemic sclerosis–related (SSc) ILD receiving mycophenolate mofetil (MMF) 1 g twice per day for 5 years (Table 1). In early April 2020, the patient developed fevers, malaise, sore throat, cough, and dyspnea after exposure to her son-in-law who tested positive by nasopharyngeal swab for SARS-CoV-2. She self-quarantined, discontinued MMF, and received supportive care at home. Her symptoms resolved after 3 weeks, and she required no … Address correspondence to Dr. E.R. Volkmann, 1000 Veteran Avenue, Ste 32-59, Los Angeles, CA 90095, USA. Email: evolkmann{at}mednet.ucla.edu.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.007 |
| 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".