MétaCan
Menu
Back to cohort
Record W3088401488 · doi:10.7759/cureus.10588

Optimizing Perimortem Cesarean Section Outcomes Using Simulation: A Technical Report

2020· article· en· W3088401488 on OpenAlexaff
Maggie O'Dea, Deanna Murphy, Adam Dubrowski, Peter Rogers

Bibliographic record

VenueCureus · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsOntario Tech UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineInterpersonal communicationObstetrics and gynaecologyPatient safetyHarmMedical educationDebriefingPregnancyPsychology

Abstract

fetched live from OpenAlex

Simulation-based medical education (SBME) is an educational technique that enables participants to experience an immersive representation of a clinical event for the purpose of practice, learning, and evaluation. This experience is intended to improve trainees' competency and confidence in both procedural tasks, as well as team-based and interpersonal skills when responding to real-world clinical encounters. Moreover, SBME improves procedural exposure and competency in low-frequency, high-stakes clinical procedures without the risk of adverse consequences, error, or patient harm - a priority for physician training at all levels. This technical report describes a novel bi-phasic maternal cardiac arrest simulation that can be used to teach and train post-graduate year one (PGY1) emergency medicine and obstetrics and gynecology trainees in the use of perimortem cesarean sections (PMCS) prior to in-situ exposure. Using a high-fidelity simulation protocol employing training manikins and 3-D printed models of gravid uteri, this bi-phasic simulation, completed over two sessions, six months apart, will equip trainees with the knowledge, skills, and professionalism behaviors necessary for difficult clinical decisions and time-critical procedures.

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)0.000
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 score0.137
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.062
GPT teacher head0.355
Teacher spread0.293 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCureusSame topicCardiac Arrest and ResuscitationFrench-language works237,207