Evaluating the Impact of Stopping Chronic Therapies after Modulator Drug Therapy in Cystic Fibrosis: The SIMPLIFY Clinical Trial Study Design
Bibliographic record
Abstract
Abstract The care for individuals with cystic fibrosis (CF) with at least one F508del mutation will greatly change as a result of the unparalleled clinical benefits observed with the new triple-combination CFTR (CF transmembrane regulator)–modulator therapy elexacaftor/tezacaftor/ivacaftor (ETI). Incorporating ETI into the standard of care creates new motivation and opportunity to consider reductions in overall treatment burden and evaluate whether other chronic medications can now be safely discontinued without loss of clinical benefit. SIMPLIFY is a master protocol poised to test the impact of discontinuing versus continuing two commonly used chronic therapies in people with CF who are at least 12 years of age or older and stable on ETI therapy. The protocol is composed of two concurrent randomized controlled trials designed to evaluate the independent short-term effects of discontinuing hypertonic saline or dornase alfa, enabling individuals on both therapies to participate in one or both trials. The primary objective for each trial is to determine whether discontinuing treatment is noninferior to continuing treatment after establishment of ETI, as measured by the 6-week absolute change in the percent-predicted forced expiratory volume in 1 second. Developing this study required a balance between ideal study-design principles and feasibility. SIMPLIFY will be the largest multicenter, randomized, controlled medication-withdrawal study in CF. This study is uniquely positioned to provide timely evidence on whether the daily treatment burden can be reduced among individuals on CFTR-modulator therapy. Clinical trial registered with www.clinicaltrials.gov (NCT 04378153).
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".