Global lung function initiative: Reference equations for the transfer factor for carbon monoxide (TLCO)
Bibliographic record
Abstract
Background: There are numerous reference equations available for transfer factor for carbon monoxide (TLCO); however there are currently no recommendations as to which equation should be used in clinical practice. We aimed to develop The Global Lung Function Initiative (GLI) all-age, multi-ethnic reference values for TLCO. Methods: Data from 19 centres in 14 countries from studies published after the year 2000 were combined. Reference equations were derived using the LMS method and the GAMLSS program. Results: A total of 12 660 TLCO measurements from asymptomatic, lifetime non-smokers from 4 to 91 years of age were available. Eighty-five percent of the data submitted were from Caucasian subjects. Following adjustments for elevation above sea level, and the assumptions used for calculating the anatomic dead space volume there was a high degree of overlap among the datasets. All data were uncorrected for haemoglobin. Reference equations were derived for TLCO, carbon monoxide update from the lung (KCO) and alveolar volume (VA). The limited data in non-Caucasians limited the development of equations for Caucasians subjects only. Conclusion: This is the largest collection of normative TLCO data, and the first robust global reference equation available for TLCO. The variations among disparate TLCO datasets can be reduced by taking into account methodological considerations. Results are presented on behalf of the GLI TLCO working group.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".