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Acupuncture treatment of pregnant women with low back and pelvic pain - an intervention study

2010· article· en· W2152473129 on OpenAlexfundaboutno aff
Lena Ekdahl, Kerstin Petersson

Bibliographic record

VenueScandinavian Journal of Caring Sciences · 2010
Typearticle
Languageen
FieldMedicine
TopicPregnancy-related medical research
Canadian institutionsnot available
FundersMcGill University
KeywordsMedicineAcupuncturePhysical therapyMcGill Pain QuestionnaireVisual analogue scalePelvic painPregnancyIntervention (counseling)PopulationObstetricsAlternative medicineNursingSurgery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.328
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2010
Admission routes2
Has abstractyes

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