Effects of deprescribing from inhaled corticosteroids in people with cystic fibrosis: protocol for a target trial emulation using the UK CF Registry
Bibliographic record
Abstract
INTRODUCTION: Observational data are increasingly used to study and draw causal inferences about the effects of treatments. Target trial emulation (TTE) is a framework for mitigating biases in causal investigations through specification of an observational study, targeting a specific causal research question, based on a real or hypothetical randomised controlled trial. Investigations into the effects of treatment discontinuation are of growing interest and particularly relevant in cystic fibrosis (CF), where treatment burden is high and new transformative therapies are becoming widespread. We aim to use the TTE framework to investigate the effect of discontinuation of inhaled corticosteroids (ICS) on clinical outcomes in people with CF. Our observational emulation will be based on the CF WISE (Withdrawal of Inhaled Steroids Evaluation) trial (PMID:16556691). METHODS AND ANALYSIS: Two study designs proposed for investigating treatment effects using observational data are the prevalent new-user design and the sequential trials design. Each design uses different but related methods to address similar causal questions; however, the comparability between them remains uncertain. We will conduct a population-based cohort study using data from the UK CF Registry between January 2016 and June 2018 and apply these designs. We will specify the target trial protocol for each study design. Estimates for the causal effects of discontinuing ICS will be obtained and compared with those from the CF-WISE trial. ETHICS AND DISSEMINATION: This study has received approval from the UK CF Registry Research Committee for both the research and access to data. Ethical approval has also been granted by the LSHTM Ethics Committee. The UK CF Registry has NHS Research Ethics Committee approval (REC reference: 24/EE/0012). The findings from this project will be submitted to peer-reviewed journals and presented at academic conferences.
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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 | 0.177 | 0.202 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.075 | 0.023 |
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".