Does Integration of Various Ion Channel Measurements Improve Diagnostic Performance in Cystic Fibrosis?
Bibliographic record
Abstract
RATIONALE: The diagnosis of cystic fibrosis (CF) may remain inconclusive despite comprehensive evaluation. OBJECTIVES: Determine whether combined ion channel measurements (C-ICMs) obtained from different end-organ epithelia can help diagnose CF. METHODS: Prospective enrollment of (1) a training sample of 156 non-CF subjects and 107 patients with CF, and (2) a validation cohort of 202 patients with single-organ CF-like phenotypes. All subjects had genotyping, sweat test, and nasal potential difference (NPD). Principal components analysis was applied to derive various candidate C-ICMs by combining sweat chloride plus every one or two combination(s) of four NPD parameters (maximal potential difference [MaxPD], change in potential difference in response to perfusion with amiloride [ΔAmil], change after chloride-free and isoproterenol perfusion [ΔCl-free+Iso], and total change in potential difference [ΔAmil+Cl-free+Iso]). MEASUREMENTS AND MAIN RESULTS: The best of the 10 candidate C-ICMs, which combined sweat chloride and two NPD parameters (ΔCl-free+Iso and ΔAmil+Cl-free+Iso), diagnosed CF in the training sample with 100% sensitivity and specificity (CF cutoff > 0). In the validation cohort, C-ICM was normal in all subjects with normal sweat test and normal/borderline NPD, with the exception of one subject. C-ICM was abnormal in all subjects when the sweat test was abnormal and the NPD was abnormal/borderline, and when the sweat test was borderline and the NPD was abnormal. C-ICM was abnormal in 75 and 85.7% of subjects with normal sweat chloride plus abnormal NPD, and those with abnormal sweat test plus normal NPD, respectively. In borderline sweat test cases, 23.5, 90, and 100% of subjects had abnormal C-ICM with normal, borderline, and abnormal NPD, respectively. CONCLUSIONS: The concept of combining different measures of cystic fibrosis transmembrane conductance regulator function into a single composite score is feasible. The C-ICM may be useful for diagnostic determination of patients with questionable CF.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".