Cardiopulmonary exercise testing during pregnancy
Bibliographic record
Abstract
The goal of this review is to examine practical considerations when conducting cardiopulmonary exercise testing during pregnancy. In a clinical and research setting, cardiopulmonary exercise testing during pregnancy is valuable in identifying underlying cardiopulmonary conditions, stratifying the risk of adverse pregnancy outcomes, as well as establishing exercise tolerance/limitations. This review encompasses information regarding the unique physiological adaptations that occur throughout gestation (e.g., changes in resting heart rate, blood pressure, glucose, etc.) and how these adaptations impact the interpretation of physiological measurements. There are also key concerns that are unique to pregnant populations that should be considered when participating in exercise (i.e., fetus, ventilation, thermoregulation, urinary incontinence, low back pain, and pelvic girdle pain). This step-by-step review of cardiopulmonary exercise testing outlines pregnancy related adjustments to standardized methods (i.e., screening/documentation, pre- and post-test measurements, protocol specifics, modality selection, and fetal monitoring) which should be considered for the safety of both the participant and fetus. Currently, pregnancy specific exercise testing guidelines are lacking. Therefore, we will be discussing the limitations of current recommendations such as a safe cut off for resting heart rate and pregnancy specific test termination criteria.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".