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Record W2752343679 · doi:10.1089/end.2017.0436

Randomized Controlled Trial Comparing Three Different Modalities of Lithotrites for Intracorporeal Lithotripsy in Percutaneous Nephrolithotomy

2017· article· en· W2752343679 on OpenAlexaff
Nadya York, Michael S. Borofsky, Ben H. Chew, Casey A. Dauw, Ryan F. Paterson, John D. Denstedt, Hassan Razvi, Robert B. Nadler, Mitchell R. Humphreys, Glenn M. Preminger, Stephen Y. Nakada, Amy E. Krambeck, Nicole L. Miller, Colin Terry, Lori D. Rawlings, James E. Lingeman

Bibliographic record

VenueJournal of Endourology · 2017
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsWestern University
FundersCook MedicalBoston Scientific CorporationNorthwestern UniversityVanderbilt University
KeywordsMedicinePercutaneous nephrolithotomyRandomized controlled trialLithotripsyRandomizationSurgeryPercutaneousUltrasoundRadiology

Abstract

fetched live from OpenAlex

PURPOSE: To compare the efficiency (stone fragmentation and removal time) and complications of three models of intracorporeal lithotripters in percutaneous nephrolithotomy (PCNL). MATERIALS AND METHODS: cartridges. Since the StoneBreaker lacks an ultrasonic component, it was used with the LUS-II ultrasonic lithotripter to allow fair comparison with combination devices. RESULTS: /min in the Lithoclast Select and Cyberwand groups, respectively. After statistically adjusting for the smaller mean stone in the StoneBreaker group, there was no difference in the stone clearance rate among the three groups (p = 0.249). Secondary outcomes, including complications and stone-free rates, were similar between the groups. CONCLUSIONS: The Cyberwand, Lithoclast Select, and the StoneBreaker lithotripters have similar adjusted stone clearance rates in PCNL for stones >2 cm. The safety and efficacy of these devices are comparable.

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)0.002
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
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 head0.044
GPT teacher head0.332
Teacher spread0.288 · 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 designRandomized trial
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

Citations32
Published2017
Admission routes1
Has abstractyes

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