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Record W1526397089 · doi:10.5489/cuaj.886

Outpatient tubeless percutaneous nephrolithotomy:the initial case series

2013· article· en· W1526397089 on OpenAlexaffvenue
Darren Beiko, Linda Lee

Bibliographic record

VenueCanadian Urological Association Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsPercutaneous nephrolithotomyMedicineSeries (stratigraphy)SurgeryPercutaneousGeneral surgeryGeology

Abstract

fetched live from OpenAlex

INTRODUCTION: Percutaneous nephrolithotomy (PCNL) has traditionally been performed on an inpatient basis. To the best of our knowledge, this is the first report of tubeless PCNL on a completely outpatient basis. The purposes of this study were to assess the safety and efficacy of outpatient PCNL. METHODS: We reviewed the initial consecutive outpatient tubeless PCNLs performed at our institution by a single surgeon. Patients were discharged home the day of surgery only after meeting strict discharge criteria. Preoperative, intraoperative and postoperative data were collected prospectively. RESULTS: Outpatient tubeless PCNL was performed in 3 patients. The mean maximum stone diameter was 14 mm. The average hospital stay was 175 minutes. All 3 patients were discharged home in stable condition after meeting all of the inclusion criteria. There were no emergency room visits or hospital readmissions postoperatively. The mean follow-up period was 47 days. All stones were calcium oxalate and the stone free rate was 100%. There were no minor or major complications. CONCLUSION: In properly selected patients, outpatient tubeless PCNL is safe and effective. Our initial experience with outpatient PCNL has been favourable and warrants further investigation in a larger patient population.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.246
Teacher spread0.231 · 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 designCase report
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

Citations55
Published2013
Admission routes2
Has abstractyes

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Same venueCanadian Urological Association JournalSame topicKidney Stones and Urolithiasis TreatmentsFrench-language works237,207