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Record W2168062284

Impact of cognitive imaging and sex differences on the development of laparoscopic suturing skills.

2005· article· en· W2168062284 on OpenAlexaff
Tyrone Donnon, Jean Descôteaux, Claudio Violato

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineConcordanceContext (archaeology)CognitionAnalysis of varianceTest (biology)Internal medicinePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The introduction of noninvasive laparoscopic surgery has raised concerns about appropriate teaching techniques for medical students considering surgery as a specialization. The principal aim of this study was to determine the effect, between the sexes, of cognitive imaging as a teaching method in the context of learning a surgical technique. METHODS: A randomized treatment-control sample of 42 medical student volunteers was used to test the effect of cognitive imaging on performance and on traditional instructional techniques to help medical students acquire suturing skills specific to laparoscopic surgery. RESULTS: Repeated-measures analysis of variance showed no significant effect for the use of cognitive imaging (F1,40 = 0.97, p > 0.05). Males tended to perform better than females in completing tasks that required the use of visual-spatial manipulation of the instruments within a simulated laparoscopic environment (F1,40 = 5.08, p < 0.05). CONCLUSIONS: These results, which are in concordance with other research findings, indicate that females generally have lower visual-spatial abilities than males. Enhanced performance for both sexes, however, increases rapidly with practice. Other than verbal one-on-one instruction, males on average rank instructional approaches that are applied and visual higher than do females.

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 score0.446
Threshold uncertainty score0.119

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 head0.034
GPT teacher head0.293
Teacher spread0.259 · 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

Citations57
Published2005
Admission routes1
Has abstractyes

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