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Record W2107948499

Visual-spatial ability, learning modality and surgical knot tying.

2006· article· en· W2107948499 on OpenAlexaffabout
Michael G. Brandt, Edward T. Davies

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsWestern University
Fundersnot available
KeywordsKnot tyingMedicineTyingTest (biology)Artificial intelligenceSurgeryComputer science
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: The ability to mentally rotate an object in 3 dimensions has been shown with an individual's score on the Vandenberg and Kuse Mental Rotations Test. The was to determine whether this Mental Rotations Test could be used to predict performance complex surgical skill - the tying of a 1-handed surgical reef knot. In addition, we learning a spatially complex surgical skill could be achieved more effectively via a computer-based selfdirected learning approach than with a didactic lecture-based teaching method. METHODS: preclerkship medical students at the University of Western Ontario were randomized into computer-based self-directed learning group and a didactic lecture-style learning group. administration of the Mental Rotations Test, the students were taught how to tie a reef knot via the learning modality assigned to their respective group. RESULTS: Students Mental Rotations Test scores were able to tie more surgical knots in the allocated time Students learning how to tie the surgical knot via the computer-based self-directed showed improvement on their knot tying abilities more rapidly than their didactically trained colleagues. CONCLUSION: The ability to mentally rotate an object in 3 dimensions played an important initial learning of a spatially complex surgical technical skill. Our data demonstrated learning was as effective and more practical than traditional lecture-based learning.

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 score0.461
Threshold uncertainty score0.319

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 head0.027
GPT teacher head0.284
Teacher spread0.257 · 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

Citations56
Published2006
Admission routes2
Has abstractyes

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