The added value of haemoglobin to height, age, and sex to predict DLCO in subjects with preserved exercise capacity
Bibliographic record
Abstract
BACKGROUND: The single breath diffusion capacity for carbon monoxide (DLCO) captures several aspects of the role of the lung in meeting the metabolic demands of the body. The magnitude of the independent contributors to the DLCO is unknown. The aim of this study was to investigate the factors that independently contribute to the DLCO. OBJECTIVES: The objective was to investigate the impact of height, age, sex and haemoglobin on DLCO, alveolar volume (VA) and carbon monoxide transfer coefficient (KCO). METHODS: Study participants were pre-screened based on normal exercise capacity achieved during an incremental cardio-pulmonary exercise testing (CPET) using cycle ergometry at McMaster University Medical Center between 1988-2012. Participants who had an FEV1>80% predicted, with an FEV1/FVC ≥0.7 and who achieved a maximum power output ≥80% were selected for analysis. In total, 16,298 subjects [61% male, mean height 1.70m (range 1.26-2.07), age 49 yrs (10-94), weight 79 kg (23-190) had DLCO measured while demonstrating normal spirometry and exercise capacity. RESULTS: The DLCO increased exponentially with height, was 15% greater in males, increased with age yearly until 20, then decreased yearly after the age of 35, and was 6% higher per gram of haemoglobin (5.58*Height(m)1.69*1.15 in Males*(1-0.006*Age>35)*(1+0.01*Age<20) *(1+0.06*Hb gm/dl), (r = 0.76). CONCLUSION: Height, age, sex, and haemoglobin all have independent influence on the DLCO in subjects with normal spirometry and preserved exercise capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".