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Record W3198511380 · doi:10.5489/cuaj.7286

PEG-coated patch parenchymal closure technique and initial outcomes during minimally invasive partial nephrectomy

2021· article· en· W3198511380 on OpenAlexaffvenueabout
Ryan McLarty, Benjamin Beech, Jan K. Rudzinski, Blair St. Martin, Howard Evans

Bibliographic record

VenueCanadian Urological Association Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicHemostasis and retained surgical items
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineNephrectomySurgeryPEG ratioRetrospective cohort studySingle CenterProspective cohort studyBlood transfusionBlood lossKidneyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: is a novel polyethylene glycol-coated (PEG-coated) collagen patch that acts as a topical hemostatic agent. It has been applied to a variety of surgical techniques. Here, we present our series and technique using a PEG-coated patch for minimally invasive partial nephrectomy (MIPN). METHODS: We conducted a retrospective chart review of all patients undergoing MIPN by a single surgeon at a tertiary Canadian medical center between July and December 2018. We included patients if a PEG-coated patch was used to close the renal parenchymal defect. We also describe in detail our technique for laparoscopic patch deployment. RESULTS: A total of 17 patients met inclusion criteria, of whom 12 were male. Mean age was 63 years old. Median size of renal mass was 2.85 cm in largest dimension and median RENAL score was 6. Hilar clamping was carried out in 12 (70.9%) cases, with an average warm ischemic time of 16.1 minutes. The remainder of cases were performed off-clamp. Median estimated blood loss was 238 mL and the mean change in hemoglobin on postoperative day 1 compared to preoperatively was 21.2 g/L. The average length of stay was 1.76 days. No patient required blood transfusion or underwent angioembolization or re-operation for bleeding within 90 days. There were no Clavien-Dindo grade 3 or greater complications. CONCLUSIONS: A PEG-coated patch can be used safely and effectively in lieu of traditional two-layer renorrhaphy with acceptable outcomes. Larger, prospective series are required to ascertain its true value and cost-effectiveness.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.047
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
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 head0.016
GPT teacher head0.255
Teacher spread0.239 · 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

Citations2
Published2021
Admission routes3
Has abstractyes

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