Association between lumbopelvic pain, disability and sick leave during pregnancy – a comparison of three Scandinavian cohorts
Bibliographic record
Abstract
OBJECTIVE: To explore the association between disability and sick leave due to lumbopelvic pain in pregnant women in 3 cohorts in Sweden and Norway and to explore possible factors of importance to sick leave. A further aim was to compare the prevalence of sick leave due to lumbopelvic pain. DESIGN/SUBJECTS: Pregnant women (n = 898) from two cohorts in Sweden and one in Norway answered to questionnaires in gestational weeks 10–24; two of the cohorts additionally in weeks 28–38. METHODS: Logistic regression models were performed with sick leave due to lumbopelvic pain as dependent factor. Disability, pain, age, parity, cohort, civilian status, and occupational classification were independents factors. RESULTS: In gestational weeks 10–24 the regression model included 895 cases; 38 on sick leave due to lumbopelvic pain. Disability, pain and cohort affiliation were associated with sick leave. In weeks 28–38, disability, pain and occupation classification were the significant factors. The prevalence of lumbopelvic pain was higher in Norway than in Sweden (65%, vs 58% and 44%; p < 0.001). CONCLUSION: Disability, pain intensity and occupation were associated to sick leave due to lumbopelvic pain. Yet, there were significant variations between associated factors among the cohorts, suggesting that other factors than workability and the social security system are also of importance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".