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Record W2791218621 · doi:10.1097/dcr.0000000000001044

Rectal Dissection Simulator for da Vinci Surgery: Details of Simulator Manufacturing With Evidence of Construct, Face, and Content Validity

2018· article· en· W2791218621 on OpenAlexaff
George Melich, Ajit Pai, Ramy Shoela, Kunal Kochar, Supriya Patel, John Park, Leela M. Prasad, Slawomir J. Marecik

Bibliographic record

VenueDiseases of the Colon & Rectum · 2018
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of British ColumbiaRoyal Columbian Hospital
Fundersnot available
KeywordsContent validityLikert scaleConstruct validityMedicineFace validityTotal mesorectal excisionPhysical therapyScale (ratio)SurgeryPsychometricsPsychologyClinical psychologyPatient satisfaction

Abstract

fetched live from OpenAlex

BACKGROUND: Apprenticeship in training new surgical skills is problematic, because it involves human subjects. To date there are limited inanimate trainers for rectal surgery. OBJECTIVE: The purpose of this article is to present manufacturing details accompanied by evidence of construct, face, and content validity for a robotic rectal dissection simulation. DESIGN: Residents versus experts were recruited and tested on performing simulated total mesorectal excision. Time for each dissection was recorded. Effectiveness of retraction to achieve adequate exposure was scored on a dichotomous yes-or-no scale. Number of critical errors was counted. Dissection quality was tested using a visual 7-point Likert scale. The times and scores were then compared to assess construct validity. Two scorer results were used to show interobserver agreement. A 5-point Likert scale questionnaire was administered to each participant inquiring about basic demographics, surgical experience, and opinion of the simulator. Survey data relevant to the determination of face validity (realism and ease of use) and content validity (appropriateness and usefulness) were then analyzed. SETTINGS: The study was conducted at a single teaching institution. SUBJECTS: Residents and trained surgeons were included. INTERVENTION: The study intervention included total mesorectal excision on an inanimate model. MAIN OUTCOME MEASURES: Metrics confirming or refuting that the model can distinguish between novices and experts were measured. RESULTS: A total of 19 residents and 9 experts were recruited. The residents versus experts comparison featured average completion times of 31.3 versus 10.3 minutes, percentage achieving adequate exposure of 5.3% versus 88.9%, number of errors of 31.9 versus 3.9, and dissection quality scores of 1.8 versus 5.2. Interobserver correlations of R = 0.977 or better confirmed interobserver agreement. Overall average scores were 4.2 of 5.0 for face validation and 4.5 of 5.0 for content validation. LIMITATIONS: The use of a da Vinci microblade instead of hook electrocautery was a study limitation. CONCLUSIONS: The pelvic model showed evidence of construct validity, because all of the measured performance indicators accurately differentiated the 2 groups studied. Furthermore, study participants provided evidence for the simulator's face and content validity. These results justify proceeding to the next stage of validation, which consists of evaluating predictive and concurrent validity. See Video Abstract at http://links.lww.com/DCR/A551.

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)0.004
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.126
GPT teacher head0.320
Teacher spread0.195 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations11
Published2018
Admission routes1
Has abstractyes

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