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Record W4405542028 · doi:10.4081/aiua.2024.12862

Analysis of the top-down HoLEP learning curve: A single-center experience of two clinical fellows

2024· article· en· W4405542028 on OpenAlexaff
Karim Daher, Moustafa Fathy, Amr Hodhod, Parsa Nikoufar, Abdulrahman Alkandari, Loay Abbas, Ruba Abdul Hadi, Hazem Elmansy

Bibliographic record

VenueArchivio Italiano di Urologia e Andrologia · 2024
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsThunder Bay Regional Research InstituteNOSM UniversityThunder Bay Regional Health Sciences Centre
Fundersnot available
KeywordsMedicineEnucleationSupervisorDemographicsSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: Holmium laser enucleation of the prostate (HoLEP) is known to have a steep learning curve. The top-down technique was introduced to lessen the number of procedures required to master HoLEP. We aimed to present the experiences of two successive clinical fellows with the top-down HoLEP learning curve and compare their performance with the supervisor. METHODS: We conducted a prospective study of 40 patients who underwent top-down HoLEP performed by two successive fellows at our institution from September 2020 to November 2022. Before data collection, each learner observed three top-down HoLEP procedures and assisted with seven additional cases before independently performing top-down HoLEP under supervision. We collected data from each fellow's first 20 consecutive top-down HoLEP procedures. The learners' cases were grouped according to chronological order (Cases 1-10 and 11-20). The primary outcome was defined as the number of cases before the fellow could independently complete all steps of top-down HoLEP without any major intraoperative complications. The secondary outcomes included the intraoperative and postoperative outcomes of both groups. The fellows' 40 cumulative cases were then compared against retrospective data from 148 procedures conducted by their supervisor. RESULTS: There were no significant differences in patient demographics for both clinical fellows. Each learner performed the first 20 cases independently without needing the supervisor to intervene. No major intraoperative complications were recorded, and there were no statistically significant differences in intraoperative and postoperative outcomes between fellows' cases. There was a statistically significant difference between the fellows and their supervisor in terms of operative efficiency and enucleation efficiency (p < 0.001). We did not find a significant difference between the fellows and the supervisor regarding intraoperative complications, major postoperative complications, or postoperative subjective and objective parameters. CONCLUSIONS: Top-down HoLEP shows promising and reproducible results in shortening HoLEP's learning curve. Larger comparative and multi-institutional studies are warranted.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.008
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.367
Teacher spread0.317 · 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 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
Published2024
Admission routes1
Has abstractyes

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