Infection or Autoimmunity? The Clinical Challenge of Interstitial Lung Disease in Systemic Sclerosis During the COVID-19 Pandemic
Bibliographic record
Abstract
To the Editor: The novel coronavirus disease 2019 (COVID-19) pandemic is a world emergency that may inevitably complicate the clinical scenario of interstitial lung disease (ILD) secondary to systemic sclerosis (SSc)1,2. The striking similarities in computed tomography (CT) between the 2 diseases make it difficult to distinguish a worsening of SSc-ILD from a COVID-19 superinfection2. For this reason, we present a case of a 67-year-old woman affected by limited cutaneous SSc with anticentromere antibody positivity, characterized by Raynaud phenomenon for 10 years, and skin involvement but no ILD or pulmonary hypertension. She was treated with symptomatic drugs but no immunosuppressive therapy. According to national and local regulations, approval by the ethics committee is not required for a case report. We obtained the patient’s informed consent to publish the material. The patient was seen in January 2020 because of mild fever (37.5°C), malaise, and cough. First, she was treated with ampicillin/minocycline. Due to symptom persistence, she underwent a lung CT, which revealed bilateral, multilobar, rounded ground-glass opacities (GGO), in both the upper and lower lobes (Figure 1A). Initially, the upper lobe involvement raised the suspicion of a pulmonary infection. However, the predominant peripheral, symmetrical, and basal distribution of GGO areas could not rule out the suspicion of early SSc-ILD. Given the ongoing pandemic, a COVID-19 reverse transcription PCR test was performed, with … Address correspondence to Dr. M. Orlandi, Department of Clinical and Experimental Medicine, University of Florence, & Department of Geriatric Medicine, Division of Rheumatology AOUC, Padiglione 28c Ponte Nuovo, piano 1, Via delle Oblate, 4, Florence, Italy. Email: martina.orlandi{at}unifi.it.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.010 | 0.008 |
| 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".