Age- and height-based prediction bias in spirometry reference equations
Bibliographic record
Abstract
Prediction bias in spirometry reference equations can arise from combining equations for different age groups, rounding age or height to integers or using self-reported height. To assess the bias arising from these sources, the fit of 13 prediction equations was tested against the Global Lungs Initiative (GLI) dataset using spirometric data from 55,136 healthy Caucasians (54% female). The effects on predicted values of using whole-year age versus decimal age, and of a 1% bias in height, were quantified. In children, the prediction bias relative to GLI ranged from -22% to +17%. Switching equations at 18 yrs of age led to biases of between -846 (-14%) and +1,309 (+38%) mL. Using age in whole years rather than decimal age introduced biases from -8% to +7%, whereas a 1% overestimation of height introduced bias that ranged from +1% to +40%. Bias was greatest in children and adolescents, and in short elderly subjects. Using a single spirometry equation applicable across all ages and populations reduces prediction bias. Measuring and recording age and height accurately are also essential if bias is to be minimised.
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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 | 0.046 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".