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Record W1492029949

Evaluation of pain before and after vaginal delivery.

2009· article· en· W1492029949 on OpenAlexaboutno aff
Bruna Gomes Alves, Telma Mariotto Zakka, Manoel J. Teixeira, José Tadeu Tesseroli de Siqueira, Sílvia Regina Dowgan Tesseroli de Siqueira

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMcGill Pain QuestionnaireAnxietyChildbirthLabor painVisual analogue scalePregnancyVaginal deliveryObstetricsObstetrics and gynaecologyPhysical therapyPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: The objective of this pilot study was to determine pain characteristics of pregnant women immediately before and after childbirth by vaginal delivery and to compare them with the pain intensity reported by physicians. METHODS: We evaluated 20 Brazilian women between September and December 2007 with the WHOQOL-Bref instrument, VAS, McGill Pain Questionnaire, and Anxiety Adapted Scale. We interviewed the obstetrician with the VAS about the patient's pain. Data were analyzed with the chi-square test. RESULTS: Mean age was 22.35 years (SD = 6.24, range 15-39 years). It was necessary to use oxytocin in 15 (75%) patients, which had no correlation with anxiety degree. Higher intensity of pain (p < 0.05) and higher anxiety index (p < 0.05) were more common in women in the first pregnancy. CONCLUSIONS: Higher pain intensity was associated with higher anxiety levels (p < 0.05). Around half of the obstetricians' VAS scores were lower than the VAS scores of women, and probably pain at labor was underestimated and not controlled. Higher indices of anxiety and pain were associated, and were more frequent in women in the first pregnancy.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.316
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2009
Admission routes1
Has abstractyes

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