Delphi Consensus Recommendations on Management of Dosing, Adverse Events, and Comorbidities in the Treatment of Idiopathic Pulmonary Fibrosis with Nintedanib
Bibliographic record
Abstract
PURPOSE: Nintedanib is an approved treatment for idiopathic pulmonary fibrosis (IPF), which slows disease progression. Management of patients with IPF receiving nintedanib can be complicated by tolerability issues, comorbidities, and concomitant medications. We developed consensus recommendations on the management of dosing, adverse events and comorbidities in patients with IPF treated with nintedanib. METHODS: A modified Delphi process using 3 questionnaires was used to survey 14 pulmonologists experienced in using nintedanib. Panelists rated their agreement with statements on a Likert scale from -5 (strongly disagree) to +5 (strongly agree). Consensus was predefined as a mean score of ⩽-2.5 or ⩾+2.5 with a standard deviation not crossing zero. RESULTS: The panelists' recommendations were largely aligned with clinical trial data, real-world evidence, and the prescribing information, and provided additional guidance regarding minimizing gastrointestinal effects, periodic monitoring for liver dysfunction, caution with respect to concomitant administration of cytochrome P450 3A4 and P-glycoprotein 1 inhibitors and inducers and anticoagulants, and management of comorbidities. The panelists unanimously agreed that adverse event management should be individualized, based on careful consideration of the risks and benefits of each possible intervention and discussion with the patient. CONCLUSIONS: These consensus recommendations provide additional guidance on the appropriate management of IPF with nintedanib, for use alongside evidence-based literature and the prescribing information.
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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 | 0.312 | 0.303 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".