Assessing accuracy of testing and diagnosis in cystic fibrosis
Bibliographic record
Abstract
INTRODUCTION: Next to evaluating for defects in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, diagnostic guidelines for cystic fibrosis (CF) include CFTR function tests. The primarily used sweat test and genetics generally produce straightforward CF diagnoses. However, a widened CF disease spectrum and large number of CFTR gene variants with unknown or varying clinical consequences shift reliance on CFTR functional tests to assess for CF or CFTR-related disease. Recently, CFTR functional tests are used to record efficiency of CFTR modulator drugs. AREAS COVERED: This review provides background and accuracy of the currently used CFTR functional tests, including the sweat test, nasal potential difference (NPD), and intestinal current measurements (ICM). We summarize published evidence addressing technical and biological reasons for test variability and test result in relation to CF-associated symptoms. EXPERT OPINION: The CFTR functional tests demonstrate high accuracy despite biological and technical variability. Data is scarce for ICM. Each test identifies CF from non-CF but show lower accuracy for individuals not fitting the classic CF diagnostic criteria. Adherence to standardized protocols is critical to improve test accuracy across different centers. Lastly, instead of relying on the single test results, diagnostic assessment should be based on integrating multiple functional and genetic test results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".