Unraveling complexity: A rare case of pulmonary sarcoidosis coinciding with systemic scleroderma
Bibliographic record
Abstract
We present a rare case of a 66-year-old female with systemic sclerosis and Raynaud's phenomenon, diagnosed at age 25, who was found to have multinodular pulmonary sarcoidosis. The coexistence of sarcoidosis and scleroderma, are relatively common, well-documented in medical literatures however, the relationship between the two is complex and multifactorial. She was referred to pulmonology for evaluation of a lung mass and nodule noted on chest x-ray. Chest CT revealed widespread bilateral pulmonary nodularity, more prominent on the right, with multifocal mass-like consolidation. Differential diagnoses included malignancy, infection, or inflammatory disease. A transbronchial lung biopsy of the right upper and middle lobes showed focal granulomatous inflammation with negative AFB staining, and lymph node biopsies were negative for malignancy. These findings favored a diagnosis of sarcoidosis over systemic sclerosis, which typically does not present with granulomatous inflammation. A PET CT was performed to further differentiate between inflammation and malignancy, revealing extensive perilymphatic nodularity and FDG-avid mass-like opacities, especially in the right upper and middle lobes, consistent with the sarcoid Galaxy sign. She was initiated on prednisone 5 mg and methotrexate 25 mg daily. Given the rarity of this presentation, recognizing the co-occurrence of these two autoimmune conditions was critical in reaching the correct diagnosis and initiating appropriate therapy.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| 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".