Leukocyte telomere length and mycophenolate therapy in chronic hypersensitivity pneumonitis
Bibliographic record
Abstract
Recent prospective clinical trials have shown antifibrotic therapies slow lung function decline in patients with idiopathic pulmonary fibrosis (IPF) [1, 2] and progressive fibrosing interstitial lung disease (ILD). Similar findings were demonstrated in scleroderma-associated ILD [3] despite use of the immunosuppressive therapy mycophenolate mofetil (MMF). Prospective data for the treatment of other forms of ILD, such as chronic hypersensitivity pneumonitis (CHP) are lacking. Our groups previously reported that the treatment of CHP with MMF was associated with a decreased incidence of adverse events, a reduction in prednisone dose, and improved lung function when compared to prednisone alone [4, 5], but prospective studies are needed to confirm these findings. Short leukocyte telomere length (TL) is associated with increased mortality in patients with ILD, including CHP and IPF [6–8]. A recent investigation also showed TL may influence the response to immunosuppressive therapy. In that study, patients with IPF and short TL had a higher risk of death, lung transplantation, and forced vital capacity (FVC) decline, when exposed to immunosuppressive therapy, including MMF [9]. In this investigation we sought to determine whether similar findings occurred in patients with CHP. We hypothesised that patients with CHP and short TL would experience a higher prevalence of death and disease progression when compared to those with longer TL. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of interest: Dr. Arinze reports grants from MSD, outside the submitted work;. Conflict of interest: Dr. Adegunsoye reports personal fees from Genentech, personal fees from Boehringer Ingelheim, outside the submitted work;. Conflict of interest: Dr. Morisset has nothing to disclose. Conflict of interest: Dr. Newton has nothing to disclose. Conflict of interest: Dr. Oldham reports personal fees from Boehringer Ingelheim, personal fees from Genentech, outside the submitted work;. Conflict of interest: Dr. Vittinghoff has nothing to disclose. Conflict of interest: Dr. Linderholm has nothing to disclose. Conflict of interest: Dr. Strek reports grants from Boehringer Ingelheim, grants from Galapagos, grants from Novartis, outside the submitted work;. Conflict of interest: Dr. Noth reports grants and personal fees from Boehringer Ingelheim, personal fees from Intermune, personal fees from Anthera, personal fees from GSK, personal fees from Immuneworks, outside the submitted work;. Conflict of interest: Dr. Garcia has nothing to disclose. Conflict of interest: Dr. Wolters reports grants and personal fees from Boehringer Ingelheim, personal fees from Blade therapeutics, personal fees from Roche, grants from Genentech, outside the submitted work;. Conflict of interest: Dr. Ley reports grants from Nina Ireland Program for Lung Health, during the conduct of the study; personal fees from Genentech, outside the submitted work;.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".