Influence of a Virtual Exercise Program throughout Pregnancy during the COVID-19 Pandemic on Perineal Tears and Episiotomy Rates: A Randomized Clinical Trial
Bibliographic record
Abstract
The complications associated with COVID-19 confinement (impossibility of grouping, reduced mobility, distance between people, etc.) influence the lifestyle of pregnant women with important associated complications regarding pregnancy outcomes. Therefore, perineal traumas are the most common obstetric complications during childbirth. The aim of the present study was to examine the influence of a supervised virtual exercise program throughout pregnancy on perineal injury and episiotomy rates during childbirth. A randomized clinical trial design (NCT04563065) was used. Data were collected from 98 pregnant women without obstetric contraindications who attended their prenatal medical consultations. Women were randomly assigned to the intervention (IG, N = 48) or the control group (CG, N = 50). A virtual and supervised exercise program was conducted from 8–10 to 38–39 weeks of pregnancy. Significant differences were found between the study groups in the percentage of episiotomies, showing a lower episiotomy rate in the IG (N = 9/12%) compared to the CG (N = 18/38%) (χ2 (3) = 4.665; p = 0.031) and tears (IG, N = 25/52% vs. CG, N = 36/73%) (χ2 (3) = 4.559; p = 0.033). A virtual program of supervised exercise throughout pregnancy during the current COVID-19 pandemic may help reduce rates of episiotomy and perineal tears during delivery in healthy pregnant women.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".