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Early Postpartum Perineal Repair or C-Section Suture Removal Reduces Pain Without Wound Complications [7J]

2019· article· en· W2944775557 on OpenAlexaff
Anthony K.C. Chan

Bibliographic record

VenueObstetrics and Gynecology · 2019
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsNiagara Health System
Fundersnot available
KeywordsMedicineObservational studySurgeryVisual analogue scaleProspective cohort studyAnalgesicFibrous jointSittingAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: To describe postpartum sutures or staples related pain resolution and wound healing in patients with perineal repair or C-section who underwent early sutures or staples removal. METHODS: All patients in an outpatient obstetric clinic who delivered between June 1, 2016 to June 30, 2017 and received sutures or staples in wound closure were invited to participate in this prospective observational study of early sutures or staples removal by day four to seven postpartum. Pain scores were recorded for activities of standing, walking, and sitting from a preset scale of zero to five before and after sutures or staples removal. With six weeks postpartum visit, wound healing was evaluated. Pictures were taken from those who consented. RESULTS: A total of 68 participants were included in this prospective observational study. 52 participants underwent vaginal deliveries with perineal repair and 16 participants underwent C-sections prior to inclusion of this study. When pain scores were three or above, there was reduction of pain scores of two or more. Excellent wound healing was observed at six-week postpartum visits. CONCLUSION: Early sutures or staples removal in postpartum patients reduced pain without compromising wound healing. This suggests the utility of early sutures or staples removal in postpartum patients can reduce analgesic use and improve quality of life.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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 integrity0.0000.000
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 head0.018
GPT teacher head0.269
Teacher spread0.252 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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