Patient-specific FEV1 reproducibility limits to more accurately track disease progression
Bibliographic record
Abstract
Background: Spirometry is routinely used to guide clinical decisions for patients with respiratory disease; however, the reproducibility standards (within-subject biological variability of serial measurements) is based on limited evidence generated in small data sets. Objective: To define a patient-specific reproducibility limit for FEV1 change that represents the normal within-subject variability in health, and validate its use to monitor clinically meaningful change in a cystic fibrosis (CF) population. Methods: FEV1 Z-scores were calculated for spirometry measurements from 4 studies including 47,938 measures from 7885 healthy children 6-18 years of age. A conditional change score (Zc) (Cole 1995) was derived based on the correlation between serial zFEV1 measures, and regression to the mean. A Zc within +/- 1.96 is considered within the normal limits of reproducibility in health. Zc was then validated in CF children using FEV1 1876 measures from 64 CF patient visits in the Toronto CF Database. Results: The correlation between repeated measures in healthy children increased with age and decreased with time. In children with CF, between-visit change estimated with Zc was less biased with age, time and initial value than a relative change in FEV1 % predicted. Notably, 76% of stable–stable visit changes fell within +/- 1.96 Zc, whereas 67% were within the 10% limits. Conclusions: Zc defines the normal range of FEV1 reproducibility with less bias than using changes in percent predicted, and may provide a more accurate means to track disease progression over time. This work was funded by a Cystic Fibrosis Research Innovation Award from Vertex Pharmaceuticals
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".