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Record W1973482334 · doi:10.1155/2014/526590

Using Adhesive Glue to Repair First Degree Perineal Tears: A Prospective Randomized Controlled Trial

2014· article· en· W1973482334 on OpenAlexaff
Tomer Feigenberg, Esther Maor‐Sagie, Einat Zivi, Mushira Abu-Dia, Assaf Ben‐Meir, Hen Y. Sela, Yossef Ezra

Bibliographic record

VenueBioMed Research International · 2014
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsTrillium Health Centre
FundersHebrew University of JerusalemHadassah Medical Organization
KeywordsGLUEMedicineRandomized controlled trialSurgeryTearsExact testAdhesiveProspective cohort study

Abstract

fetched live from OpenAlex

Our objective was to evaluate the effectiveness of adhesive glue in repairing first degree perineal tears. We conducted a noninferiority prospective, randomized, controlled trial comparing adhesive glue with traditional suturing. Each case was evaluated immediately after birth and after the puerperium. The two-sample t-test and the Mann-Whitney nonparametric test were applied to compare quantitative variables between the treatment groups. The chi-squared test and Fisher's exact test were used to assess the association between qualitative variables. A total of 102 women participated, 28 in the suture arm and 74 in the adhesive glue arm. While cosmetic and functional results of adhesive glue use were not inferior to suturing, the use of adhesive glue was associated with a shorter procedure, less need for local anesthetic, less pain, and greater satisfaction. Our results suggest a novel approach for the repair of common postpartum first degree lacerations. The use of adhesive glue achieves cosmetic and functional results equal to traditional suturing and offers some immediate advantages for the patient. While further clinical trials are needed to validate our results, it is important to inform obstetrician of the possible use of adhesive glue in these very common clinical scenarios. This trial is registered with NCT00746707.

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)0.003
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0010.000

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 head0.121
GPT teacher head0.429
Teacher spread0.308 · 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.

Study designRandomized trial
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

Citations29
Published2014
Admission routes1
Has abstractyes

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