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Record W3038238809 · doi:10.7759/cureus.9020

Anatomical 3D-Printed Silicone Prostate Gland Models and Rectal Examination Task Trainer for the Training of Medical Residents and Undergraduate Medical Students

2020· article· en· W3038238809 on OpenAlexafffund
Jasmine DeZeeuw, Noel O’Regan, Christine Goudie, Michael Organ, Adam Dubrowski

Bibliographic record

VenueCureus · 2020
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of Ontario Institute of TechnologyJaneway Children's Health and Rehabilitation CentreMemorial University of Newfoundland
FundersMemorial University of Newfoundland
KeywordsMedicineTrainerProstate glandTask (project management)ProstateMedical educationMedical physicsInternal medicineManagement

Abstract

fetched live from OpenAlex

The current generation of graduating medical students is entering into practice with minimal exposure to the digital rectal examination (DRE), a necessary component of a complete physical examination. Simulation-based medical education (SBME) using anatomical silicone models and task trainers can provide hands-on training opportunities for medical students to rehearse DREs. However, there is a scarcity of affordable, validated, and anatomically correct silicone prostate models and task trainers for rehearsing DREs. This technical report describes and validates evidence for silicone prostate models and a DRE task trainer created from three-dimensional (3D)-printed molds for medical student- and resident-training and clinical skills maintenance. A pre-existing 3D human model and five different prostate models from open-source, royalty-free websites were converted using Fusion360™ (Autodesk Inc., San Rafael, CA) into stereolithography files and altered to produce negative molds. The prostate molds were filled with silicone and polylactic acid filament "nodules". The buttocks were isolated from the human model and an anal canal was designed with a larger cavity on the interior to hold the silicone prostate models to simulate a real DRE. Five practicing urologists were recruited to evaluate the 3D-printed silicone prostate models and the DRE task trainer. The participants were provided with a qualitative survey and asked to rate the perceived realism and educational effectiveness of the prostate models and task trainer. The silicone models and task trainer were found to be useful for simulation training when attempting DRE techniques. The feedback from the participants was positive overall and provided recommendations for improvement including stabilizing the prostate models in the task trainer, smoothening the transition between the rectum and the prostate, and adding an additional "normal" prostate model. Silicone prostate models and DRE task trainers created from 3D molds are economical and anatomically and tactically accurate training tools to teach and maintain DRE skills as compared to commercially available, cost-prohibitive models. After making the suggested and appropriate modifications, the prostate models and DRE task trainer could potentially be used as tools for clinical skills training and maintenance and for patient education in the future.

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)0.003
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.003

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 head0.069
GPT teacher head0.352
Teacher spread0.284 · 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
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

Citations5
Published2020
Admission routes2
Has abstractyes

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