Acupuncture treatment of pregnant women with low back and pelvic pain - an intervention study
Bibliographic record
Abstract
OBJECTIVE: To describe patients' experience of acupuncture treatment in low back and pelvic pain during pregnancy. DESIGN: An intervention study carried out between September 2000 and December 2001, involving 40 pregnant women. PARTICIPANTS: The study population consisted of healthy pregnant women presenting with low back and pelvic pain at maternity health care centres within a defined area in southern Sweden. INTERVENTION: Two groups of women received acupuncture treatment from gestational week 20 (group 1) or week 26 (group 2) respectively, for a period of 6 weeks divided into eight sessions of 30 minutes each. MEASUREMENTS: Pain assessment was carried out using Pain-O-Meter and visual analogue scale (POM-VAS), Short-Form McGill Questionnaire (SF-MPQ), Short-Form-36: Health Survey Questionnaire (SF-36), followed by telephone interviews 2-3 months after delivery. FINDINGS: The results of POM-VAS, SF-MPQ and SF-36 showed a relief of pain in both groups. In group 2, an improvement in several SF-36 variables was noted in spite of increased physical restrictions. Telephone interviews confirmed that expectations of treatment were fulfilled. Using content analysis the main category, limitations in daily life, was identified, with subcategories pain, and psychological well-being. CONCLUSION: It may be advantageous to begin acupuncture therapy later in pregnancy to maximise pain relief.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".