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Influence of non-pharmacological methods on duration of labor: a systematic review

2024· review· en· W4399744640 on OpenAlexaboutno aff
Thais Blaya Leite Gregolis, Sabrina da Silva Santos, Ilce Ferreira da Silva, Andréa Ramos da Silva Bessa

Bibliographic record

VenueCiência & Saúde Coletiva · 2024
Typereview
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsAcupressureMedicineChecklistPhysical therapySupine positionMassageAcupunctureCINAHLPhysical medicine and rehabilitationAnesthesiaPsychologyNursingAlternative medicine

Abstract

fetched live from OpenAlex

The article aims to verify the influence of MNFs on the duration of the birth process. A systematic review was carried out in the MEDLINE, Web of Science and LILACS databases, through a combination of terms that cover the topic addressed, from 1996 to 2021/April. The Excel spreadsheet was used to collect data to extract information regarding each selected article, in turn, data analysis included the evaluation and classification of quality, reliability and risk of bias, thus, the following tools were used: Cochrane RoB 2, Checklist and Newcastle-Ottawa Scale. Warm bath, walking, exercises with a birthing ball, breathing techniques, supine position, acupuncture, acupressure and water birth reduced labor time. While spontaneous pushing, massage and immersion baths prolonged labor. Non-pharmacological methods capable of reducing the duration of labor were hot/warm shower, walking, birth ball exercises, breathing techniques, maternal mobility, dorsal position, acupuncture, acupressure and water birth, as well. associated applied techniques such as hot/warm bath, ball exercises and lumbosacral massage, as well as immersion bath, ball exercises, aromatherapy, vertical postures and maternal mobility with alternating vertical postures, shortened the birth time.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.286
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.075
GPT teacher head0.498
Teacher spread0.423 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

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