Unsupervised home spirometry is not equivalent to supervised clinic spirometry in children and young people with cystic fibrosis: Results from the CLIMB‐CF study
Bibliographic record
Abstract
Abstract Background Handheld spirometry allows monitoring of lung function at home, of particular importance during the COVID‐19 pandemic. Pediatric studies are unclear on whether values are interchangeable with traditional, clinic‐based spirometry. We aimed to assess differences between contemporaneous, home (unsupervised) and clinic (supervised) spirometry and the variability of the former. The accuracy of the commercially available spirometer used in the study was also tested. Methods Data from participants in the Clinical Monitoring and Biomarkers to stratify severity and predict outcomes in children with cystic fibrosisc (CLIMB‐CF) Study aged ≥ 6 years who had paired (±1 day) clinic and home forced expiratory volume in 1 s (FEV 1 ) readings were analyzed. Variability during clinical stability over 6‐months was assessed. Four devices from Vitalograph were tested using 1 and 3 L calibration syringes. Results Sixty‐seven participants (median [interquartile range] age 10.7 [7.6−13.9] years) provided home and clinic FEV 1 data pairs. The mean (SD) FEV 1 % bias was 6.5% [±8.2%]) with wide limits of agreement (−9.6% to +22.7%); 76.2% of participants recorded lower results at home. Coefficient of variation of home FEV 1 % during stable periods was 9.9%. Data from the testing of the handheld device used in CLIMB‐CF showed a potential underread. Conclusion In children and adolescents, home spirometry using hand‐held equipment cannot be used interchangeably with clinic spirometry. Home spirometry is moderately variable during clinical stability. New handheld devices underread, particularly at lower volumes of potential clinical significance for smaller patients; this suggests that supervision does not account fully for the discrepancy. Opportunities should be taken to obtain dual device measurements in clinic, so that trend data from home can be utilized more accurately.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".