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How Hormonal Contraceptive Use Impacts Laparoscopic Skill Acquisition in Biologically Female Medical Students

2018· article· en· W3176994671 on OpenAlexaboutno aff
Elizabeth J. Olive, Jennifer Klei, Victoria A. Roach

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsnot available
Fundersnot available
KeywordsTestosterone (patch)MedicinePsychologyPhysiologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION Hormonal contraceptive use is commonplace among biological females of reproductive age. Biological females typically have lower testosterone levels than males, and hormonal contraceptive use lowers that testosterone level even further. Higher testosterone levels are shown to be positively correlated with spatial ability and, ultimately, with better performance in surgical skill acquisition. Biological females, particularly those using hormonal contraceptives, may then be at a disadvantage for learning new surgical skills. However, the relationship between hormonal contraceptive use and surgical skill acquisition is not yet understood, and will be examined in this study. METHODS Participants (anticipated n=50) are students of Oakland University William Beaumont School of Medicine and are participating under approval of the university's Institutional Review Board. This study makes use of laparoscopic box trainers and the peg transfer task from the MISTELS (McGill Inanimate System for Training and Evaluation of Laparoscopic Skills) battery of tests. The control and treatment groups are divided based on biologically female participants who do not use hormonal contraceptives, and those who do, respectively. Both groups perform several screening tasks regarding demographics, handedness, video game usage, mental rotation ability, and ovulation status. Participants then train on the peg transfer task, recording their times for each attempt until proficiency is reached. An ANCOVA, 2 (cycle/static) × 1 (frequency of repetitions) with spatial ability as a covariate, with appropriate post‐hocs as necessary, will be used to statistically analyze differences in the learning curves between naturally cycling participants and those using hormonal contraceptives. ANTICIPATED RESULTS Compared to the naturally cycling group, the group using hormonal contraceptives is expected to require greater time and number of attempts to reach proficiency on the peg transfer task. SIGNIFICANCE The results are expected to support the hypothesis that hormonal contraceptive use is negatively correlated with surgical skill acquisition. This may have implications for informing contraceptive choices of biological females pursuing careers in surgery. In addition, it may inform future curriculum design for graduate medical programs, to maximize learning opportunities for aspiring surgeons. Support or Funding Information Department of Biomedical Sciences Research Seed Fund, Oakland University William Beaumont School of Medicine. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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 head0.041
GPT teacher head0.340
Teacher spread0.299 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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