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Record W4311537948 · doi:10.1016/j.urolvj.2022.100199

Utilization of Swiss LithoClast® Trilogy Lithotripter During Percutaneous Nephrolithotomy

2022· article· en· W4311537948 on OpenAlexaff
Matthew Lee, Mark Assmus, Nicholas Dean, Amy E. Krambeck

Bibliographic record

VenueUrology Video Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPercutaneous nephrolithotomyMedicineTroubleshootingSuctionSurgeryPercutaneousBiomedical engineeringComputer scienceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Percutaneous nephrolithotomy (PCNL) is the gold standard for surgical treatment of large kidney stones. Although there are a variety of commercially available Lithotrites, the objective of this video is to review technical aspects of the EMS Lithoclast Trilogy™ and to discuss tips for troubleshooting as well as set up of the device. PCNL is recommended as first-line therapy for renal pelvic stones that are greater than 2 cm in size. Furthermore, PCNL should also be considered for lower pole stones that are > 1 cm in size. The Trilogy uses a combination of ballistic and ultrasound energy to efficiently fragment stone. It is easy to set up. Tips to maximize efficiency of the device including priming the device and appropriate selection of the probe size. We prefer using lower frequency settings at the initiation of the case to suction out blood clot. Once the stone is identified we increase the frequency and impact settings. The surgeon should not torque on the handle as the probe will not function as efficiently and can even break prematurely. The Trilogy is a dual energy lithotrite available in a wide range of probe sizes. Bench and clinical data suggest it is equivalent or more efficient than other available Lithotrites. The surgeon should take care not to torque on the probe to ensure the probe does not break prematurely and functions at maximum efficiency.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.024
GPT teacher head0.285
Teacher spread0.262 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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