Determination of maximal voluntary ventilation in children with cystic fibrosis
Bibliographic record
Abstract
Maximal voluntary ventilation (MVV) may be determined directly by the sprint method or calculated from pulmonary function data, using the functions MVV = forced expired volume in 1 sec (FEV(1)) x 35 or MVV = FEV(1) x 40. The purpose of this paper was to test the validity of the equation over a wide range of lung function in children. Cystic fibrosis (CF), a chronic lung disease where children typically have a wide range of pulmonary function, was chosen as the study requirement. Spirometric data from 332 children with CF who underwent pulmonary function testing between 1987-2000 were stratified according to disease severity, and box-plots comparing the ratio of MVV to FEV(1) for each category were generated. As results indicated that the equation underestimates true MVV proportionally to the degree of airflow limitation, a new function to predict MVV for this population was derived and tested. The new equation was derived using data from patients who were tested on odd-numbered days (group A). The validity of the new equation was then tested on the patients tested on even-numbered days (group B). To test its validity, the results were compared to the "gold standard" sprint values using a Bland and Altman plot. MVV was expressed as a function of FEV(1) and predicted FEV(1): MVV = 27.7(FEV(1)) + 8.8(PredFEV(1)) (R(2) = 0.98, P < 0.05). In this way, the accuracy of the new equation was confirmed. Whenever possible, we recommend MVV be determined by the sprint method in accordance with ATS guidelines. If this is not feasible, we recommend considering the new prediction equation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".