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Record W2258851440 · doi:10.1089/gyn.2015.0082

Unidirectional Barbed Suture for Vaginal-Cuff Closure in Laparoscopic and Robotic Hysterectomy

2016· article· en· W2258851440 on OpenAlexaboutno aff
Douglas Brown, Joseph M. Gobern

Bibliographic record

VenueJournal of Gynecologic Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicUterine Myomas and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBarbed sutureSurgeryDehiscenceCuffFibrous jointHysterectomyWound dehiscenceRetrospective cohort study

Abstract

fetched live from OpenAlex

Objective: The aim of this research was to estimate the incidence of vaginal-cuff dehiscence in patients undergoing total laparoscopic hysterectomy (TLH) or TLH with robotic assistance, using a unidirectional barbed suture compared to a vicryl suture. Materials and Methods: For this retrospective cohort study (Canadian Task Force Classification II-3), a total of 474 patient records were reviewed for women who underwent either TLH or TLH with robotic assistance. The procedures were performed by experienced minimally invasive gynecologic surgeons at a tertiary-care university-based teaching hospital and a community hospital from July 2010 to November 2012. Results: The overall incidence of vaginal-cuff dehiscence was 0.6%. There were 3 patients in the unidirectional barbed suture group diagnosed with vaginal cuff dehiscence, (0.8%, 95% CI: 0.2 to 2.4%), compared with no vaginal-cuff dehiscence in the delayed absorbable suture group (0.0%, 95% CI: 0.0% to 3.2%). One patient had recurrent dehiscence. There were no significant differences in postoperative bleeding, cellulitis, granulation tissue, or overall complications between the unidirectional and conventional suture groups (2.7% versus 1%; p = 0.47). Conclusions: Use of a unidirectional barbed suture for vaginal-cuff closure during TLH or TLH with robotic assistance does not increase the risk of vaginal-cuff dehiscence. (J GYNECOL SURG 32:167)

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)0.001
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 score0.011
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.026
GPT teacher head0.280
Teacher spread0.253 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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