Correlations between the active straight leg raise, sleep and somatosensory sensitivity during pregnancy with post-partum lumbopelvic pain: an initial exploration
Bibliographic record
Abstract
Background and aims For some women, lumbopelvic pain (LPP) developed during pregnancy becomes a continuing post-partum problem. Increased understanding of potential prognostic factors is required. This study investigated whether active straight leg raise (ASLR), sleep dysfunction and pressure pain sensitivity during pregnancy are correlated with LPP intensity and quality, disability, and physical health-related quality of life (HRQoL) post-partum. Methods An exploratory, prospective cohort study design was used. Baseline factors of interest were: (1) ASLR, (2) Pittsburgh Sleep Quality Index, and (3) pressure pain thresholds (PPTs) collected from pregnant women from sites local and distal to the lumbopelvic area. Follow-up data collected 11-18 months post-partum (n=29) were: (1) pain intensity score (numerical rating scale), (2) pain quality (McGill Pain Questionnaire), (3) disability (Pelvic Girdle Questionnaire), and (4) HRQoL (36-item Short Form Health Survey). Correlation analysis was performed. Results Greater difficulty with an ASLR during pregnancy correlated with lower post-partum physical HRQoL scores (r=-0.563, p=0.002). Likewise, reduced PPTs at the sacrum during pregnancy was correlated with a higher post-partum pain quality score (r=-0.384, p=0.040). Conclusions In this cohort, findings indicate that poor ASLR performance and localised pressure pain hypersensitivity at the pelvis during pregnancy are correlated with post-partum physical HRQoL and pain quality, respectively. Implications Pain sensitivity may contribute to the prognosis of women with LPP during pregnancy. These explorative findings may be important for designing larger prognostic studies and may assist in directing potential pain management in post-partum LPP.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".