Rapidly Progressive Systemic Sclerosis–associated Interstitial Lung Disease After Intravesical Bacillus Calmette-Guérin Therapy for Early-stage Bladder Cancer
Bibliographic record
Abstract
To the Editor: Despite recent advances in the management of systemic sclerosis–associated interstitial lung disease (SSc-ILD), it remains the most common cause of death and a significant contributor to morbidity.1,2 SSc-ILD is characterized by a wide spectrum of disease courses, with some patients having limited nonprogressive fibrosis, whereas others develop rapid and extensive fibrosis leading to respiratory failure.3 Although the pathogenesis of SSc is still poorly understood, extensive fibrosis can result from an inappropriate chronic inflammatory response to a specific trigger. Toxic air pollutants or infections can shape the immune system toward an immunostimulatory status. Here we report the case of a patient with diffuse cutaneous SSc (dcSSc) who developed rapidly extensive ILD after bacillus Calmette-Guerin (BCG) therapy given for noninvasive bladder cancer. Written informed consent was obtained from the patient and ethics board approval was received (Comite de Protection des Personnes CPP Ile de France III, no. 2008-A00624-51). A 61-year-old woman with dcSSc with speckled antinuclear antibodies and no antigen-specific antibodies presented with aggravating dyspnea. dcSSc had been diagnosed in 2003 with severe skin disease (peak modified Rodnan skin score [mRSS] at 40/51) and was …
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".