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MP67-12 ASSOCIATION OF BUCCAL MUCOSAL GRAFT HISTOLOGY AND MOUTH ANATOMY TO BULBAR URETHROPLASTY GRAFT TAKE AND FACIAL MORBDITY

2019· article· en· W2941443925 on OpenAlexaboutno aff
Shyam Sukumar, Cooper R. Benson, Debduth Pijush, Carlos Pagan, Steven B. Brandes

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicUrological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineUrethroplastyHistologyAnatomyBuccal administrationMouth mucosaBuccal mucosaSurgeryPathologyOral cavityDentistryUrethra

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyTrauma/Reconstruction/Diversion: Urethral Reconstruction (including Stricture, Diverticulum) III (MP67)1 Apr 2019MP67-12 ASSOCIATION OF BUCCAL MUCOSAL GRAFT HISTOLOGY AND MOUTH ANATOMY TO BULBAR URETHROPLASTY GRAFT TAKE AND FACIAL MORBDITY Shyam Sukumar*, Cooper Benson, Debduth Pijush, Carlos Pagan, and Steven Brandes Shyam Sukumar*Shyam Sukumar* More articles by this author , Cooper BensonCooper Benson More articles by this author , Debduth PijushDebduth Pijush More articles by this author , Carlos PaganCarlos Pagan More articles by this author , and Steven BrandesSteven Brandes More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000557007.54803.62AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Buccal mucosal grafts (BMGs) are the standard graft material for urethroplasty. Graft take is dependent on a proper host bed and graft. Quality of BMGs can be variable. A leak on post op voiding cystourethrography (VCUG)is believed to be from poor graft take. The effect of BMG histology or oral health or graft take is unknown. The role of oral health or mouth dimensions on postoperative facial morbidity is unknown. METHODS: Prospective review of 10 patients undergoing augmentation urethroplasty with BMG for bulbar strictures. Pre-op and post-op day 1 and 3 wks, patients completed oral health questionnaires: The Kayser-Jones Brief Oral Health Status Exam (BOHSE), McGill Pain Questionnaire (McGill),Oral Health Impact Profile Questionnaire (OHIP 14), and Oral Patient Reported Outcomes Measures(PROMS). Mouth dimensions and measurements were also obtained. Post-op VCUGs were evaluated for leak at 3 weeks. Histology of harvested BMGs were assessed by staff pathologist (CAP) using calibrated eyepiece to measure thickness of each anatomic layer, and grade the graft by a validated oral mucosal inflammation and ulceration index (Oral Mucositis Index). RESULTS: Mean age 38.7yrs, Q max 6.1 ml/s, IPSS 22, SHIM 17. Types of urethroplasty: Palmintieri double buccal urethroplasty-3, dorsal onlay with ventral inlay-1, combined ventral bulbar BMG with dorsal penile BMG-1, dorsal BMG-2, augmented anastomotic-2, Asopa-1. Mean pre-op oral health scores were low or normal. Mean McGill and Oral PROMS scores at POD 1, 2.1 and 2.0, and at 3 weeks, 17.9 and 17.7, respectively. Mean pre-op mouth dimensions: opening, 4.9 cm (4.5-6.0) and commissure to TMJ length- 4.1 cm (3.5-8). Mean size of BMG harvested = 4.8 x 1.6 cm, and on stretch 5.4 x 2.0 cm; mean delta 9.8% (0-28%)and 18.1%(0-34%),respectively. Patients with the highest pain and oral PROMS scores post op had bilateral BMGs or the smallest mouth opening and shortest length. Mean microscopic thickness of each layer of BMG: epithelium- 692µ(500-1200), lamina propria- 97µ(50-200), and submucosa-1093 µ(400-1900). Average mucositis score 2.9(0-11)and BMG friability - 2 (1-3). The patient with a VCUG leak had the highest mucositis index score, lowest SHIM and thickest submucosa. CONCLUSIONS: Harvested BMG vary in quality as to elasticity, thickness, friability and histology. Smaller mouth dimensions appear to be associated with worse post-operative morbidity and pain. Worse BMG histology as to mucositis and submucosa thickness appear to negatively affect graft take. A larger multi-institutional study is currently underway. Source of Funding: NONE New York, NY; New Orleans , LA; New York, NY© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e972-e972 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Shyam Sukumar* More articles by this author Cooper Benson More articles by this author Debduth Pijush More articles by this author Carlos Pagan More articles by this author Steven Brandes More articles by this author Expand All Advertisement PDF downloadLoading ...

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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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.166
Threshold uncertainty score0.556

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1660.032

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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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".

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Published2019
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