Ventilatory efficiency during pregnancy: the influence of obesity
Bibliographic record
Abstract
We examined the effect of pregnancy and obesity on ventilatory efficiency at rest and during exercise in women of child‐bearing age. We hypothesized that obesity would have a detrimental effect on ventilatory efficiency compared to the normal weight pregnant and non‐pregnant populations. Forty pregnant women (20 normal weight, PG; and 20 obese, PGOB) between 16 and 20 weeks of gestation and 14 non‐pregnant controls (NP) performed a progressive treadmill test to volitional fatigue. Oxygen consumption (VO 2 ), minute ventilation (V E ) and an index of ventilatory efficiency (V E /VCO 2 ratio) were compared at rest and at peak exercise (PKE). PGOB exhibited a hyperventilation at rest (V E = 14.5±2.5 L/min) compared to PG (12.7±2.7 L/min, P<0.001) and NP (11.5±1.6 L/min, P<0.001) despite a lower VO 2 (P<0.05). Ventilatory efficiency was reduced ( higher V E /VCO 2 ratio ) in obese subjects at rest (4.1±0.7) and at PKE (3.0±0.4) compared to NP (2.3±0.3 and 1.8±0.2 respectively; P<0.001) and PG (2.7±0.3 and 2.1±0.2 respectively; P<0.001). Further, BMI was inversely related to efficiency across all subjects at rest (r 2 =0.83, P<0.001) and at PKE (r 2 =0.72, P<0.001). These data indicate that obesity results in a hyperventilation and decreased ventilatory efficiency that is unrelated to pregnancy. This may have implications for exercise capacity and fetal health during pregnancy. Funded by HSFO, OGS and CIHR.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".