Influence of dynamic sitting on back and pelvic pain during pregnancy
Bibliographic record
Abstract
Objective: The aim of this pilot study was to investigate the impact of dynamic sitting on the level of back and pelvic pain during pregnancy, as well as on mobility and quality of life during pregnancy. Participants and methods: The study included 22 pregnant women who were divided into an experimental and a control group. The experimental group (N = 11), with an average age of 32.55 ± 5.5 years and an initial body mass index (BMI) of 24.81 ± 3.26, used a dynamic cushion while sitting for 20 minutes daily over a period of two months. The control group (N = 11), with an average age of 31.64 ± 5.12 years and an initial BMI of 24.27 ± 2.4, did not use the dynamic cushion. The effect was assessed using a shortened version of the McGill pain questionnaire and a questionnaire evaluating the mobility and quality of life of pregnant women, known as the Pregnancy mobility index. Results: According to the shortened version of the McGill pain questionnaire, there was improvement in part of the pain assessment score in the experimental group and a deterioration of pain in the control group. The difference between the groups was not statistically significant. According to the questionnaire evaluating mobility and quality of life during pregnancy, there was a deterioration in the control group. The difference between the groups was not statistically significant. Conclusions: The use of a dynamic cushion cannot be unequivocally recommended as a preventive measure for back pain or for preserving mobility and quality of life during pregnancy. However, partial results from the pilot study suggest that further exploration of this issue is warranted, and additional research should be conducted, particularly with a larger sample size.
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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 | 0.002 |
| 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) | 0.001 | 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".