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Record W2083001788 · doi:10.1089/end.2006.0435

Durability of Endourologic Skills: Two-Year Follow-Up Study

2007· article· en· W2083001788 on OpenAlexaff
Suman Chatterjee, Sidney B. Radomski, Edward D. Matsumoto

Bibliographic record

VenueJournal of Endourology · 2007
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsToronto Western HospitalUniversity Health NetworkSt. Joseph’s Healthcare HamiltonMcMaster UniversitySt. Joseph's Hospital
Fundersnot available
KeywordsMedicineUreteroscopyChecklistUreterSurgery

Abstract

fetched live from OpenAlex

PURPOSE: To assess the long-term durability of endourologic skills among urology trainees after an intensive technical skills training course. SUBJECTS AND METHODS: Seventeen urology residents participated in a 2-day ureteroscopy course at a surgical skills center. Residents performed rigid ureteroscopy and basket manipulation of a small midureteral stone. Performance was assessed immediately after the course and 1 year and 2 years after training. Residents prospectively tracked all ureteroscopic cases in which they were considered the primary surgeon (i.e., performed greater than 75% of the procedure). Performance was measured using a validated global rating score (GRS), checklist score (CLS), and time required to complete the task. RESULTS: Overall, GRS improved over the 2-year follow-up (P < 0.001), with most of the improvement occurring in the first year (P = 0.03). The CLS and time to complete the task did not change (P = 0.08 and 0.12, respectively). At the 2-year follow-up, the number of cases logged had no significant effect on performance. CONCLUSIONS: Ureteroscopy skills are retained and continue to improve 2 years after completing an intense training session that uses high-fidelity bench models. Ureteroscopic experience is important for the maintenance and development of skills, even though they appear to plateau after 1 year. This result may also reflect a ceiling effect of the assessment tools.

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)0.001
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 score0.033
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.000
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 head0.034
GPT teacher head0.348
Teacher spread0.314 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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