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Record W4386028569 · doi:10.3390/jcm12165393

The Influence of Physical Activity during Pregnancy on Miscarriage—Systematic Review and Meta-Analysis

2023· review· en· W4386028569 on OpenAlexaff
Rubén Barakat, Dingfeng Zhang, Cristina Silva‐José, Miguel Sánchez‐Polán, Evelia Franco Álvarez, Michelle F. Mottola

Bibliographic record

VenueJournal of Clinical Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicPregnancy-related medical research
Canadian institutionsChildren’s Health Research Institute
Fundersnot available
KeywordsMedicineMiscarriageMeta-analysisPregnancyObstetricsRelative riskRecurrent miscarriageChildbirthConfidence intervalInternal medicine

Abstract

fetched live from OpenAlex

Miscarriage is an inability to complete the normal process of pregnancy and childbirth and represents a major concern for pregnant women that can be an emotionally devastating event. While it has been suggested that engaging in strenuous physical activity might be associated with an elevated risk of miscarriage, there is a recent systematic review that suggested that prenatal exercise is not associated with fetal mortality. The aim of this systematic review and meta-analysis (SR + MA) was to assess the effects of physical activity during pregnancy on the likelihood of experiencing a miscarriage (Registration No.: CRD42022370629). Thirteen randomized clinical trials (3728 pregnant women) were included. Meta-analyses were conducted with the dependent variable being the miscarriage ratio in each study. The total risk ratio (RR) sum was calculated using a random effects model. The I2 statistic was utilized to quantify the heterogeneity observed in the results. No significant association between exercise during pregnancy and the occurrence of miscarriage was found (RR = 0.83 95% CI = 0.83 (0.49–1.41); z = 0.69, p = 0.49; I2 = 0.00%, Heterogeneity p = 0.91). Results of the present SR + MA showed no increase in miscarriage risk in those who engaged in low- to moderate-intensity exercise compared to those who did not.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.113
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.325
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.113
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0250.007
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.364
GPT teacher head0.573
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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