A BEAT-PCD consensus statement: a core outcome set for pulmonary disease interventions in primary ciliary dyskinesia
Bibliographic record
Abstract
Background: Consistent use of reliable and clinically appropriate outcome measures is a priority for clinical trials, with clear definitions to allow comparability. We aimed to develop a core outcome set (COS) for pulmonary disease interventions in primary ciliary dyskinesia (PCD). Methods: A multidisciplinary international PCD expert panel was set up. A list of outcomes was created based on published literature. Using a modified three-round e-Delphi technique, the panel was asked to decide on relevant end-points related to pulmonary disease interventions and how they should be reported. First, inclusion of an outcome in the COS was determined. Second, the minimum information that should be reported per outcome. The third round finalised statements. Consensus was defined as ≥80% agreement among experts. Results: During the first round, experts reached consensus on four out of 24 outcomes to be included in the COS. Five additional outcomes were discussed in subsequent rounds for their use in different subsettings. Consensus on standardised methods of reporting for the COS was reached. Spirometry, health-related quality-of-life scores, microbiology and exacerbations were included in the final COS. Conclusion: This expert consensus resulted in a COS for clinical trials on pulmonary health among people with PCD.
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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 | 0.265 | 0.216 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".