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Record W2912529759 · doi:10.7759/cureus.3991

Development of an Anatomical Silicone Model for Simulation-based Medical Training of Obstetric Anal Sphincter Injury Repair in Bangladesh

2019· article· en· W2912529759 on OpenAlexafffundabout
Christine Goudie, Atamjit Gill, Jessica Shanahan, Andrew Furey, Adam Dubrowski

Bibliographic record

VenueCureus · 2019
Typearticle
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsMemorial University of Newfoundland
FundersAtlantic Canada Opportunities Agency
KeywordsMedicineAnal sphincterSimulation trainingSphincterSurgerySimulation

Abstract

fetched live from OpenAlex

Advancing global healthcare in developing countries has traditionally been an area of interest for many North American medical organizations, as they strive to improve patient outcomes by helping to control disease and death-related illnesses. Women's healthcare in developing countries, in particular, presents a unique set of complexities, revealing high maternal mortality statistics surrounding pregnancy, labor, and childbirth, which is often tied to home births without medically trained attendants. In September 2018, Team Broken Earth, a Canadian-based outreach initiative, hosted a three-day women's healthcare course in Dhaka, Bangladesh, which included simulation-based training stations, for the purpose of advancing clinical skills and education in regards to local labor and delivery. The training stations included the prevention of shoulder dystocia, helping babies breathe, the application of uterine compression sutures, and the repair of obstetric anal sphincter injuries (OASIS). The OASIS management station provided an opportunity to practice anal sphincter repair on anatomically accurate silicone models, which was a focus of the training course due to the high frequency of such injuries in rural Bangladesh. Evaluation surveys were supplied to workshop participants to capture their feedback about the use of the OASIS models and their efficacy as a training tool in Bangladesh. Overall, the models were considered superior as compared to pre-existing training methods, which traditionally involve textbook education and hands-on learning in emergency birthing scenarios by non-medically trained attendants. Two minor iterative improvements were suggested during the Team Broken Earth workshops in Dhaka, Bangladesh, with regards to improving the models for future use: (a) the ethnicity coloring of the models should be more inclusive, especially when delivering training in international countries, and (b) future silicone models should include the addition of mesh across the bottom layer to ensure participants fingers did not rupture the enclosed vaginal canal while suturing. The purpose of this technical report is to determine the efficacy of a silicone OASIS model, developed for practicing high-risk laceration repair that can occur during childbirth, which presents in higher frequency in developing countries, such as Bangladesh, due to the number of rural at-home deliveries. The original study in this series involved the investigation of silicone perineal repair models focusing on first- and second-degree lacerations, which were used at the Remote and Rural Conference in St. John's, Newfoundland, in April 2018. The facilitators distributed the first iteration of the models to conference participants and collected participant feedback, which concluded that several improvements were required to enhance the models for medical training purposes. With the iterative revisions complete, the model is now under further validation testing to determine its efficacy within simulation-based medical education (SBME) and clinical skill maintenance. This technical report is the second in the series and includes the most recent third and fourth-degree silicone models as well as all suggested improvements from previous clinical feedback.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.037
GPT teacher head0.326
Teacher spread0.289 · 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 designSimulation or modeling
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

Citations10
Published2019
Admission routes3
Has abstractyes

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