Comparison of a handheld turbine spirometer to conventional spirometry in children with cystic fibrosis
Bibliographic record
Abstract
Abstract Background In pediatric cystic fibrosis (CF) ambulatory care, handheld spirometry in individual clinic rooms would improve patient flow and potentially reduce patient‐to‐patient contact. A validation study was conducted to examine the accuracy of an entirely handheld turbine spirometer vs a standard laboratory device in pediatric CF patients. Methods Spirometric data were obtained from 76 CF patients aged less than 18 years in the ambulatory setting using the Micro Loop Spirometer (CareFusion) and compared to same‐day data from conventional laboratory spirometry. Results Linear relationships were obtained between devices, demonstrating good correlation: r = .99, .99, .97, and .82 for forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FEF25%‐75%, and peak expiratory flow, respectively (P < .001 for all). Biases (mean differences between devices) were −65 mL for FEV1 (P < .001) and −115 mL for FVC (P < .001) on the handheld. Bland‐Altman plots demonstrated scatter in bias across all volumes. Limits of agreement (defined as mean ± 2 standard deviations [SD]) were large: +189 to −319 mL for FEV1, equating to large limits of agreement for FEV1 percent predicted of +9.0% to −13.9%. For repeated measurements on the same device on different days, a larger percent SD was obtained with the handheld compared to the conventional spirometer (6.7% vs 5.1%, respectively). Importantly, a relatively large number (15%) demonstrated a decrease in FEV1 percent predicted of ≥10% on the handheld compared to conventional. Conclusions This suggests that while both devices have passed the recommendations for spirometry testing per American Thoracic Society/European Respiratory Society, handheld turbine vs conventional spirometers may not be used interchangeably in the pediatric CF population.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".