β-Adrenergic Sweat Secretion as a Diagnostic Test for Cystic Fibrosis
Bibliographic record
Abstract
RATIONALE: β-Adrenergically induced sweat secretion offers an expedient method to assess native cystic fibrosis transmembrane conductance regulator (CFTR) secretory function in vivo. OBJECTIVES: To evaluate the sensitivity, specificity, and reliability of a test based on the activity and secretory function of CFTR in the sweat gland. METHODS: Primary and validation trials with prospectively ascertained healthy control subjects, obligate heterozygotes, and patients with a CFTR-related disorder and CF (pancreatic sufficient and insufficient). MEASUREMENTS AND MAIN RESULTS: Diagnostic accuracy and reliability of β-adrenergic sweat secretory rates using an evaporimeter was assessed and compared with sweat chloride concentrations. The cholinergically stimulated mean sweat rate did not differ among groups. The mean maximal β-adrenergically stimulated sweat rate in heterozygotes was about half the rate of healthy control subjects, and completely absent in pancreatic-insufficient patients with CF and pancreatic-sufficient patients with CF (P < 0.0001). Subjects with a CFTR-related disorder showed reduced or absent β-adrenergic sweat secretion. The β-adrenergic secretory response demonstrated high diagnostic accuracy (area under a characteristic receiver-operator curve = 0.99; 95% confidence interval, 0.97-1.00) and reliability (intraclass correlation, 0.90; 95% confidence interval, 0.81-0.95). The diagnostic cutoff level for CF, derived from the primary trial, correctly identified all control subjects, heterozygotes, and patients with CF in the validation cohort, whereas concurrent sweat chloride measurements misclassified one heterozygote and five subjects with CF. The cholinergic and β-adrenergic sweat secretion rates were lower in women compared with men (P < 0.001). CONCLUSIONS: β-Adrenergic sweat secretion rate determined by evaporimetry is an accurate and reliable technique to assess different levels of CFTR function and to identify patients with CF.
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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 | 0.001 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".