Utilising 129Xe-MRI to determine an FEV1/FVC range of uncertainty to detect airways disease
Bibliographic record
Abstract
Introduction: An FEV1/FVC z-score of -1.64 (as defined from a healthy population), is the threshold at which airflow limitation is deemed significant, yet 129Xe-MRI often detects obstructive lung disease when FEV1/FVC is >-1.64. Here, using 129Xe-MRI, we determined an alternative FEV1/FVC range to detect impairment. Methods: People with a diagnosis of asthma and/or COPD were assessed in the NOVELTY ADPro study. The ventilation defect percentage (VDP) was calculated from 129Xe-MRI and FEV1/FVC from spirometry. ROC analysis was used to determine the FEV1/FVC z-score range from 90% sensitivity to 90% specificity to detect abnormal VDP (>2%). Within this range we then calculated the proportion of abnormal VDP in people with and without a >5 pack-year smoking history. Results: 154 patients were assessed. Mean (SD) age = 59 (13)yrs, FEV1/FVC = -1.7 (1.4)z-score, VDP = 7.8 (8.3)%. 71% and 48% had abnormal VDP and FEV1/FVC respectively. To detect abnormal VDP, an FEV1/FVC z-score of -0.58 on ROC gave 90% sensitivity (48% specificity), whilst a z-score of -1.9 gave 91% specificity (55% sensitivity). There were 58 patients within this range, 65% of whom had abnormal VDP and 48% were smokers. 89% of smokers within this range of uncertainty had abnormal VDP, in comparison 43% of non smokers had abnormal VDP. Conclusions: In people with a diagnosis of airways disease, an FEV1/FVC z-score range of uncertainty from -0.58 to -1.9 gives the trade off at its margins between 90% sensitivity and 91% specificity of having abnormal VDP. In people with suspected airways disease, an FEV1/FVC z-score of <-0.58, with a smoking history, is at high risk of having 129Xe-MRI defined airways disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".