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Record W3153382937

Focal bladder neck cautery associated with low rate of post-Aquablation bleeding.

2021· article· en· W3153382937 on OpenAlexaff
Dean Elterman, Susan Foller, Burkhard Ubrig, A. Kugler, Vincent Misraï, Angelo Porreca, Dominik Abt, Kevin C. Zorn, Naeem Bhojani, Lewis Kriteman, Rahul Mehan, Michael D. McDonald, Steven A. Kaplan

Bibliographic record

VenuePubMed · 2021
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineFulgurationSurgeryProstateHemostasisHyperplasiaUrologyInternal medicineCancer
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION To determine if focal bladder neck cautery is effective in reducing bleeding following prostate tissue resection for benign prostatic hyperplasia using Aquablation. MATERIALS AND METHODS: Consecutive patients at 11 countries in Asia, Europe and North America who underwent Aquablation for symptomatic benign prostatic hyperplasia between late 2019 and January 2021 were included in the analysis. All patients received post-Aquablation non-resective focal cautery at the bladder neck. RESULTS: A total of 2,089 consecutive Aquablation procedures were included. Mean prostate size was 87 cc (range 20 cc to 363 cc). Postoperative bleeding requiring transfusion occurred in 17 cases (0.8%, 95% CI 0.5%-1.3%) and take-back to the operating room for fulguration occurred in 12 cases (0.6%, 95% CI 0.3%-1.0%). This result compares favorably (p < .0001) to the previously published hemostasis transfusion rate of 3.9% (31/801) using methods performed in the years 2014 to 2019. CONCLUSIONS: In prostates sizes averaging 87cc (range 20 cc-363 cc), Aquablation procedures performed with focal bladder neck cautery that required a transfusion postoperatively occurred in a remarkably low number of cases.

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)0.000
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 score0.416
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.023
GPT teacher head0.250
Teacher spread0.227 · 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

Citations27
Published2021
Admission routes1
Has abstractyes

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