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Record W4212873838 · doi:10.3390/uro2010007

Safety and Efficacy of Simultaneous Bilateral Percutaneous Nephrolithotomy

2022· article· en· W4212873838 on OpenAlexaff
Victor K. Wong, Colin Lundeen, Ryan F. Paterson, Kymora B. Scotland, Ben H. Chew

Bibliographic record

VenueUro · 2022
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePercutaneous nephrolithotomyBlood lossSurgeryPercutaneousRetrospective cohort studyKidney stonesPain reliefAnesthesia

Abstract

fetched live from OpenAlex

A retrospective review was conducted to evaluate intraoperative and patient outcomes following simultaneous bilateral percutaneous nephrolithotomy (SB-PCNL). Target stone characteristics, operative time, hospitalization length, post-operative complications, blood loss, opioid use, pain, and stone-free rates were evaluated. In total, 42 patients with large renal stones (>20 mm2) were identified for this study, and 38% of them achieved stone-free status with no residual fragments apparent on post-operative day one CT imaging. The maximum mean residual fragment size was 3.67 mm2 and average number of residual fragments following the procedures was 1.63. The rates of blood loss, post-operative complications, opioid use, and pain from the study cohort were similar to the reported outcomes of studies conducted by others. The potential benefits of a single procedure and anesthesia to treat bilateral stone burdens, lower total pain medication prescribed, and lower hospital costs render SB-PCNL as an attractive option in the treatment of bilateral kidney stones.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2022
Admission routes1
Has abstractyes

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