Validation of the conditional change score for FEV1 in children with asthma
Bibliographic record
Abstract
Introduction: The European Respiratory Society (ERS) recommends using the conditional change score to interpret between visit changes in FEV1 in children (Stanojevic, ERJ, 2022). The change score adjusts for the magnitude of FEV1, age and the interval of time between tests. However, the validity of this approach has not been established. The aim of this study was to compare the conditional change score with a fixed cut-off in the relative change in FEV1 percent predicted (FEV1pp) in children with asthma. Methods: We analyzed acceptable and repeatable FEV1 measurements from 235 children with asthma who were followed at The Hospital for Sick Children in Toronto between April 2019 and October 2022. We calculated between-visit changes in FEV1 using both the conditional change score and the fixed cutoff of +/- 10% FEV1pp, and compared the results. We used percent agreement and the kappa coefficient to assess the agreement between the two approaches in categorizing changes in FEV1 as stable, significantly better, or significantly worse. Results: We analyzed 490 consecutive measurements with a median time between visits of 133 days. The mean (SD) FEV1 z-score was -0.92 (1.3). Overall, there was strong agreement between the conditional change score and the fixed cutoff of +/- 10% FEV1pp, with a percent agreement of 89.4% and a kappa coefficient of 0.77. The agreement was similar when the threshold for the conditional change score was decreased to +/- 1.65 (agreement 87.6%, kappa 0.77). Conclusion: In children with asthma, both the conditional change score and a relative change of +/- 10% FEV1pp can both be used to interpret between visit changes in lung function.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".