Pulmonary sarcoid-like granulomatosis during tumour necrosis factor-alpha inhibition: a scoping review
Bibliographic record
Abstract
Background: Sarcoidosis is characterized by non-necrotizing epithelioid granulomatous inflammation. Sarcoid-like granulomatous disease has been associated with tumor necrosis factor-α (TNF-α) inhibitor therapy. Methods: We conducted a systematic search of MEDLINE, EMBASE, Scopus, and Web of Science. Two investigators screened all abstracts and selected articles for full-text review. Disagreements were resolved by a 3rd investigator. Results: Our search generated 825 titles and 146 full text articles and conference abstracts were reviewed. Sixty-four published reports describing 86 cases were included. Articles were limited to single case reports or small case series describing biopsy-confirmed sarcoid-like pulmonary granulomatous disease following TNF-α inhibition. The most frequent underlying indications for TNF-α use were rheumatoid arthritis (n=43, 50%), ankylosing spondylitis (n=12, 14%), and Crohn’s disease (n=11, 13%). The implicated TNF-α inhibitor was etanercept in 36 (41%), adalimumab in 26 (30%), infliximab in 22 (25%), and golimumab in 2 cases (2%). Seventeen cases (20%) had isolated bilateral hilar adenopathy and 69 (80%) had parenchymal involvement. Thirty-eight cases (44%) resolved after discontinuation of the TNF-α inhibitor. However, corticosteroids were used in 40 cases (47%) either empirically or due to persistent symptoms. Conclusions: This is the first scoping review to compile and summarize sarcoid-like pulmonary disease related to TNF-α inhibition. Increased awareness could aid in more prompt recognition and appropriate drug withdrawal for those who experience pulmonary sarcoid-like granulomatosis during TNF-α inhibitor therapy.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".