Pelvic pain during pregnancy is associated with asymmetric laxity of the sacroiliac joints
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate the association between pregnancy-related pelvic pain (PRPP) and sacroiliac joint (SIJ) laxity. METHODS: A cross-sectional analysis was performed in a group of 163 women, 73 with moderate or severe (PRPP+) and 90 with no or mild (PRPP-) PRPP at 36 weeks of pregnancy. SIJ laxity was measured by means of Doppler imaging of vibrations in threshold units (TU). Pain, clinical signs and disability were assessed with visual analog scale (VAS), posterior pelvic pain provocation (PPPP) test, active straight leg raise (ASLR) test, and Quebec back pain disability scale (QBPDS), respectively. RESULTS: Mean SIJ laxity in the PRPP+ group was not significantly different from the PRPP- group (3.0 versus 3.4 TU). The mean left-right difference, however, was significantly higher in the PRPP+ group (2.2 TU) than in the PRPP- group (0.9 TU). In the PRPP- group, only 4% had asymmetric laxity of the SIJs in contrast to 37% of the PRPP+ group. Between the PRPP+ subjects with asymmetric and symmetric laxity of the SIJs significant differences were found with respect to mean VAS for pain (7.9 versus 7.0), positive PPPP test (59% versus 35%), positive ASLR test (85 versus 41%) and mean QBPDS score (61 versus 50). CONCLUSIONS: Increased SIJ laxity is not associated with PRPP. In fact, pregnant women with moderate or severe pelvic pain have the same laxity in the SIJs as pregnant women with no or mild pain. However, a clear relation between asymmetric laxity of the SIJs and PRPP is found.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".