The association between musculoskeletal pain during pregnancy and pregnancy outcomes: A systematic review and meta-analyses
Bibliographic record
Abstract
OBJECTIVE: To systematically investigate the association between musculoskeletal pain during pregnancy and birth outcomes including caesarean section, newborn birthweight, newborn birth length, and gestational age at birth. METHODS: Medline, Embase, Web of Science, Cinahl and Scopus were systematically searched to identify eligible studies. Odds ratios, mean differences, and confidence intervals were used to describe results. Risk of Bias was assessed using the Newcastle-Ottawa Scale for observational studies. GRADE (The Grading of Recommendation Assessment, Development, and Evaluation) was used to assess the quality of each outcome. RESULTS: Seven studies were included with a total population of 85,991 participants. There is low- quality evidence that pregnant women with musculoskeletal pain had 1.59 greater odds to experience delivery by caesarean section compared to those without musculoskeletal pain ([OR] 1.59, 95 % confidence interval [CI] 1.09 to 2.31). Both newborn birth weight (Mean Difference [MD] 77.79 g, 95 % [CI] -23.09 to 178.67) and newborn birth length ([MD] 0.55 cm, 95 % [CI] -0.47 to 1.56) were not affected by musculoskeletal pain, with very low-quality and low-quality evidence, respectively. There was moderate evidence that pregnant women with musculoskeletal pain had shorter gestational age (weeks), although the effect was small and possibly not clinically relevant ([MD] -0.41, 95 % [CI] -0.41 to -0.07). CONCLUSION: Pregnant women experiencing musculoskeletal pain are at greater odds of delivering their babies via caesarean than those without musculoskeletal pain, however, musculoskeletal pain during pregnancy does not appear to affect newborn birth weight, length, or gestational age at birth.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".