Sweat conductivity for diagnosing cystic fibrosis after positive newborn screening: prospective, diagnostic test accuracy study
Bibliographic record
Abstract
OBJECTIVE: To assess the accuracy of sweat conductivity among newborns and very young infants. DESIGN: Prospective, population-based, diagnostic test accuracy study. SETTING: Public Statewide Newborn Screening Programme where the incidence rate of cystic fibrosis (CF) is ≈1:11 000. PATIENTS: Newborns and very young infants with positive two-tiered immunoreactive trypsinogen. INTERVENTIONS: Sweat conductivity and sweat chloride were performed simultaneously, on the same day and facility by independent technicians, with the cut-off values of 80 mmol/L and 60 mmol/L, respectively. MAIN OUTCOME MEASURES: Sensitivity, specificity, positive and negative predictive values (PPV and NPV), overall accuracy, positive and negative likelihood ratios (+LR, -LR) and post (sweat conductivity (SC)) test probability were calculated to assess SC performance. RESULTS: 1193 participants were included, 68 with and 1108 without CF, and 17 with intermediate values. The mean (SD) age was 48 (19.2) days, ranging from 15 to 90 days. SC yielded sensitivity of 98.5% (95% CI 95.7 to 100), specificity of 99.9% (95% CI 99.7 to 100), PPV of 98.5% (95% CI 95.7 to 100) and NPV of 99.9% (95% CI 99.7 to 100), overall accuracy of 99.8% (95% CI 99.6 to 100), +LR of 1091.7 (95% CI 153.8 to 7744.9) and -LR of 0.01 (95% CI 0.00 to 0.10). After a positive and negative sweat conductivity result, the patient's probability of CF increases around 350 times and drops to virtually zero, respectively. CONCLUSION: Sweat conductivity had excellent accuracy in ruling in or ruling out CF after positive two-tiered immunoreactive trypsinogen among newborns and very young infants.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".