Meta-analysis of exhaled nitric oxide (FeNO) with or without blood eosinophils for asthma diagnosis in adults
Bibliographic record
Abstract
RATIONALE: Asthma diagnosis is challenging as spirometry and bronchial provocation tests (BPT) are often non-diagnostic or inaccessible. Type-2 biomarkers identify high-risk, treatment-responsive phenotypes, but their diagnostic value is unclear. AIM: Assess FeNO-alone or FeNO plus a blood eosinophil count (FeNO+BEC) for asthma diagnosis. METHODS: A systematic review of studies of FENO±BEC for asthma diagnosis based on bronchodilator response and BPT (methacholine-equivalent PC20<8mg⋅ml⁻¹/PD20<200μg = positive) was conducted. Meta-analysis used multiple-threshold (FeNO-alone) and bivariate random effects (FeNO+BEC) models in adults. Receiver operating characteristics’ area under the curve (AUC) and thresholds with positive likelihood ratio (+LR>10) and specificity (>90%) were determined with 95% confidence intervals (CI). PRISMA was followed. RESULTS: Of 3957 studies, 17 (n=4520) assessed FeNO, while 5 (n=2689) assessed FeNO+BEC. FeNO showed good diagnostic accuracy (AUC 0.80 [95%CI: 0.75-0.84]); at >46ppb, specificity was 96 [95-98]%, +LR 10.1 [6.29-16.34]. FeNO+BEC modestly increased AUC: 0.84 [0.83-0.86](Figure). CONCLUSION: FeNO alone demonstrates good diagnostic accuracy for asthma, with FeNO>46 ppb ruling in the diagnosis. Combining FeNO+BEC did not significantly increase test accuracy. Using FeNO may accelerate and improve diagnostic trajectories. PROSPERO#CRD42023489738; FUNDING: FRQS-APQ erj;66/suppl_69/PA1468/F1 F1 F1
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.055 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".