Reference equations for the interpretation of forced expiratory and plethysmographic measurements in infants
Bibliographic record
Abstract
Abstract Background Pulmonary function testing is commonly performed for diagnosis and clinical management of respiratory diseases. It is important to use appropriate reference equations from healthy subjects for interpretation of data from infants with lung disease. This study aimed to determine if published reference equations were similar to forced flow measures and plethysmographic infant pulmonary function testing data collected in the Canadian Healthy Infant Longitudinal Development (CHILD) Study. Methods Reference equations for five pulmonary function variables (FEV0.5, FVC, FEF25‐75, FEV0.5/FVC ratio and plethysmography (FRCpleth)) were developed using data from the nSpire system. New reference equations developed using healthy data from the CHILD Study were compared to previously published reference equations for forced flow and plethysmographic measures. Results The current analysis included 131 infants (on 181 test occasions) with forced flow measures and 161 infants (on 246 test occasions) with plethysmography measures, aged 3–24 months. Age and length were major determinants of both forced flow and plethysmography measures. In addition, ethnicity (Caucasian vs non‐Caucasian) was significantly associated with FEV0.5/FVC and FEF25‐75 measures. We found that the published reference equations based on custom‐built equipment or commercially available systems provided poor fit to our current pulmonary function testing data, resulting in placing a large proportion of our healthy population outside the normal ranges. Conclusions Our current data support the need for population and device specific reference data for infant pulmonary function studies. By deriving new equipment‐specific reference equations for our healthy population, we provide normative data to other centers utilizing this equipment.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".