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Record W4200466529 · doi:10.1016/j.urolvj.2021.100112

Thulium fiber laser enucleation of the prostate

2021· article· en· W4200466529 on OpenAlexaff
Naeem Bhojani

Bibliographic record

VenueUrology Video Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsEnucleationMedicineProstateHemostasisSurgeryUrologyInternal medicineCancer

Abstract

fetched live from OpenAlex

The new thulium fiber laser became available in North America over thepast year. The study aim of this video is to demonstrate in a stepwise approach the method ofperforming a thulium fiber laser enucleation of the prostate (TFLEP). TFLEP is indicated for men with bothersome lowerurinary tract symptoms who have evidence of bladder outlet obstruction. Gland sizecan be any size (>30grams). The patient in this video is a 72 y.o. gentleman with a past medical history of hypertension. He had failed 2 trial of voids and was catheter dependent. His prostatewas estimated to be 153.4 grams on MRI. Using the new thulium fiber laser, enucleation of the prostate can besuccessful performed. As seen in the video, there is minimal bleeding and minimal carbonization of the tissue using the no touch technique.Settings include 1J and 60Hz with the short pulse for enucleation and 1J and 30Hz with the long pulse for hemostasis. Enucleation time was 45 mins and morcellation time was 13 mins. Urine was clear in the recovery room and the patient went home with his catheter 4 hours post-surgery. The key to using this new laser for enucleation of the prostate, includes avoiding direct contact with the tissue as this will minimize carbonization. The main advantages of this new laser for enucleation of the prostate are exceptional hemostasis and excellent tissue separation. Future studies should compare this new laser with others that are currently available for prostate enucleation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.998

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.290
Teacher spread0.273 · 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 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 routes1
Has abstractyes

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