New reference equations to improve interpretation of infant lung function
Bibliographic record
Abstract
RATIONALE: With increasing use of infant pulmonary function tests (IPFTs) in both clinical and research studies, appropriate interpretation of results is essential. OBJECTIVES: To investigate the potential bias associated with "normalising" IPF by expressing results as a ratio of body size and to develop reference ranges for tidal breathing parameters, passive respiratory mechanics (compliance [Crs] and resistance [Rrs]) and plethysmographic functional residual capacity (FRCp ) for white infants during the first 2 years of life. METHODS: IPFTs were measured using the Jaeger BabyBody system and standardized protocols. Reference equations, adjusted for body size, age, and sex where appropriate, were created using multilevel modeling. RESULTS: The ratio of lung function to body length changes markedly with growth, thereby precluding its use for any outcome. While the ratio of tidal volume and Crs to body weight remained relatively constant with growth, this was not the case for FRCp . Even in healthy infants, a strong inverse relationship was observed between lung function/body weight and weight z-score which could distort interpretation of results in growth-restricted infants with lung disease, such as cystic fibrosis. Reference equations were derived from 153 healthy white infants on 232 test occasions (median age 35.5 weeks [range: 2.6-104.7]). Crown-heel length was the strongest predictor of IPF. CONCLUSIONS: When reporting IPF, use of size-corrected ratios should be discouraged, with interpretation instead based on appropriate reference equations. The current equations are applicable to white infants and young children up to 2 years of age, studied using the same commercially available equipment. The extent to which these equations are applicable to infants and young children of other ethnic backgrounds or who are tested with different equipment needs to be established.
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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 | 0.023 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".