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Record W2804563672 · doi:10.5935/2595-0118.20180030

Characterization of pain resulting from perineal trauma in women with vaginal delivery

2018· article· en· W2804563672 on OpenAlexaboutno aff
Anayhan Marques Nascimento Silva, Luciano Marques dos Santos, Érika Anny Costa Cerqueira, Evanilda Souza de Santana Carvalho, Aline Silva Gomes Xavier

Bibliographic record

VenueBrazilian Journal Of Pain · 2018
Typearticle
Languageen
FieldMedicine
TopicPregnancy-related medical research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVaginal deliveryObstetricsPregnancy

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Perineal pain in the puerperium of women with perineal traumas can affect the quality of life and interfere with normal activities and physiological needs. In addition, many obstetric practitioners neglect this symptom and an incipient scientific production about the characterization of this pain is observed. Therefore, this study aimed to compare the characteristics of perineal pain in women with perineal traumas due to episiotomy and laceration, according to the Brazilian Version of the McGill Pain Questionnaire, in a public maternity hospital in the interior of Bahia. METHODS: A cross-sectional study was carried out with 499 postpartum women who had a vaginal delivery and who presented with perineal pain associated with local traumas. RESULTS: The characterization of perineal pain was the same for both traumas, being described as “that jerk”, “boring” and “uncomfortable”. CONCLUSION: Perineal pain has considerable intensity and causes discomfort in women. Therefore, it is important that the episiotomy is performed in a restricted way and that the professionals seek to use techniques of perineal protection, as this will reduce the frequency of perineal pain and provide greater comfort to the woman in the immediate puerperium.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.277
Teacher spread0.262 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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