Prevalence and Factors Associated With Low Back Pain and Pelvic Girdle Pain During Pregnancy
Bibliographic record
Abstract
STUDY DESIGN: A cross-sectional, multicenter study. OBJECTIVE: To determine the prevalence of low back pain (LBP), leg pain (LP), and pelvic girdle pain (PGP) in pregnant Spanish women and to identify the factors associated with a higher risk. SUMMARY OF BACKGROUND DATA: Previous studies on the prevalence and risk factors for LBP and PGP have shown inconsistent results. METHODS: Sixty-one clinicians across 5 regions in Spain recruited 1158 women with a median (interquartile range) pregnancy of 35 (range, 31-38) weeks. Validated methods were used to gather data on the prevalence of LBP, LP, and PGP, anthropometric and sociodemographic characteristics, history of LBP, obstetrical history, physical activity before and during pregnancy, mattress and sleep characteristics, disability, anxiety, and depression. Separate multiple logistic regression models were developed to identify the variables associated with LBP, LP, and PGP. RESULTS: The 4-week prevalence of LBP, LP, and PGP was 71.3%, 46.2%, and 64.7%, respectively. Main factors associated with a higher likelihood of reporting pain for LBP were history of LBP related and unrelated to previous pregnancy and postpartum, pain augmenting with time spent in bed, and anxiety. Previous lumbar surgery was associated with a lower risk. The factors associated with a higher likelihood of reporting LP were reporting LBP, lower academic level, younger age, depression, a lower number of hours of sleep per day, and a higher BMI, and for PGP were higher score for depression, a higher body mass index, and a more advanced stage of pregnancy. CONCLUSION: Factors associated with a higher risk vary between LBP and PGP. History of LBP, related or not to previous pregnancy or postpartum, LBP surgery, and anxiety were the factors more strongly associated with pregnancy-related LBP. When these variables are taken into account, obstetrical data from current or previous pregnancies and other variables do not show a significant association with LBP. Stage of pregnancy and depression were associated with PGP.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".