Influence of Land or Water Exercise in Pregnancy on Outcomes
Bibliographic record
Abstract
PURPOSE: This study aimed to compare the cross-sectional results from three experimental studies conducted on land, in water, and in mixed form (land + water) during pregnancy on maternal and newborn outcomes. METHODS: A cross-sectional design was used to analyze the results of three randomized clinical trials in healthy pregnant women from Madrid (Spain) and Buenos Aires (Argentina). Five hundred and sixty-eight pregnant women were recruited. For each of the studies, the number of women in the exercise group totaled 107 for study 1 (land), 49 women for study 2 (water), and 101 women for study 3 (land + water). A total of 311 women represented the control group (CG) (pooled together from all three studies). RESULTS: Total maternal weight gain was different between study 1 and CG (11.7 vs 13.4 kg, P = 0.001, Cohen's d = 0.38) as well as the percentage of pregnant women with excessive weight gain (20.6%, n = 22, vs 37.9%, n = 118, respectively, P = 0.005, χ = 16.6, OR = 0.42, 95% confidence interval = 0.25-0.71). The number of pregnant women with gestational diabetes in CG was significantly higher than that in studies 2 and 3 (CG n = 22/7.1%; study 2, n = 0/0%; and study 3, n = 1/1%; P = 0.03, χ = 8.9). CONCLUSION: Exercise performed on land is more effective than aquatic activities in preventing excessive maternal weight gain, whereas combined programs (land + aquatic) or water exercise programs may be more effective in preventing gestational diabetes.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".