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Record W2316854287 · doi:10.1097/spv.0b013e31825e63ed

Improving Resident Competence and Knowledge Regarding Tension-Free Vaginal Tape Procedure

2012· article· en· W2316854287 on OpenAlexaff
Jeanelle Sabourin, Jane Schulz, Catherine Flood

Bibliographic record

VenueFemale Pelvic Medicine & Reconstructive Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsRoyal Alexandra HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineCompetence (human resources)Urinary incontinenceMedical physicsMedical educationSurgeryPsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to determine the effectiveness of a teaching module using simulation for the tension-free vaginal tape (TVT) procedure on procedural knowledge and skill. METHODS: Twenty-five gynecology residents participated in a teaching module about the TVT procedure and urinary incontinence, which included a simulated insertion on a training model. Questionnaires using 10-point scales for self-rated competence and knowledge and a written examination were administered before and after the module. A simulated TVT insertion was evaluated at an examination at 7 weeks and at 7 months. RESULTS: A significant median improvement of 44% on the written examination and at least one point on each of the self-rated competence and knowledge scales were observed after the teaching module. Residents performed the insertion well at both examinations (89% and 90%), regardless of surgical experience. More than 94% agreed the module was useful and improved their understanding of the procedure. CONCLUSION: A short teaching module and simulation session can effectively teach residents and improve their perceived competence with the TVT procedure.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.043
GPT teacher head0.296
Teacher spread0.253 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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