Deriving a change score for FEV1 for children with asthma
Bibliographic record
Abstract
Introduction Treatment decisions for children with asthma are often made in response to changes in lung function. Reproducibility limits may help to distinguish a clinically significant change from ‘noise’ introduced by age or time. A task force has recently highlighted the merits of FEV1 change score. Aim Derive a change score for FEV1 in children with asthma. Methods Spirometry data from five studies in asthma that measured FEV1 at ~quarterly intervals over one year were collated. A change score (aka conditional z-score for change, Zc) was calculated using standard methodology, taking into account the correlation (r) between paired measurements and both the initial age and time interval (Stanojevic S et.al, Thorax 2020). Exploratory analyses determined how r changed according to demographic and clinical factors (sex, height, weight, level of asthma control, and treatments). We further evaluated whether Zc was associated with between-visit changes in level of asthma control. Results There were 5,211 FEV1 measurements from 1,264 individuals aged 4.7-19.0y (mean=12.2y). The asthma-defined Zc was similar to the Zc published in health, (mean Zc: asthma=-0.12; health=-0.05), and it was independent of sex, height, weight, level of asthma control, and treatments. Of 4,339 pairwise measurements where asthma was controlled on both occasions, FEV1 was within the Zc reproducibility limits (<± 1.96) for 86% of cases; in comparison 74% of cases had <±10% change in FEV1% predicted. Conclusions: We have derived a change score for children with asthma. Zc was comparable between healthy children and children with controlled asthma. The relationship between Zc and future asthma outcomes needs further investigation.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".