European Respiratory Society guidelines for the diagnosis of primary ciliary dyskinesia
Bibliographic record
Abstract
The diagnosis of primary ciliary dyskinesia is often confirmed with standard, albeit complex and expensive, tests. In many cases, however, the diagnosis remains difficult despite the array of sophisticated diagnostic tests. There is no "gold standard" reference test. Hence, a Task Force supported by the European Respiratory Society has developed this guideline to provide evidence-based recommendations on diagnostic testing, especially in light of new developments in such tests, and the need for robust diagnoses of patients who might enter randomised controlled trials of treatments. The guideline is based on pre-defined questions relevant for clinical care, a systematic review of the literature, and assessment of the evidence using the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) approach. It focuses on clinical presentation, nasal nitric oxide, analysis of ciliary beat frequency and pattern by high-speed video-microscopy analysis, transmission electron microscopy, genotyping and immunofluorescence. It then used a modified Delphi survey to develop an algorithm for the use of diagnostic tests to definitively confirm and exclude the diagnosis of primary ciliary dyskinesia; and to provide advice when the diagnosis was not conclusive. Finally, this guideline proposes a set of quality criteria for future research on the validity of diagnostic methods for primary ciliary dyskinesia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".