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Record W7133023745

The Development of a Cerebral Hemispheric Surgery Simulator and its Evaluation for Epilepsy Neurosurgical Education

2022· dissertation· W7133023745 on OpenAlexfundno aff
Grace Muthoni Thiong'o

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsnot available
FundersHospital for Sick Children
KeywordsDocumentationIngenuityNoveltyEpilepsy surgeryScope (computer science)EpilepsySet (abstract data type)Learning curveVirtual reality
DOInot available

Abstract

fetched live from OpenAlex

The origins and scope of this research thesis stems from the alarming statistic that 10.1 million people worldwide are potential surgical candidates. This number is projected to increase by 1.4 million annually. Epilepsy surgery requires mastery of an eloquent set of skills which are sometimes hard to acquire in a work-hour restricted surgical training environment. Simulation in surgical education has been endorsed as a valid supplement to clinical surgical training through a reduction in the technical learning curve. The scope of the research within this manuscript, on a macro-scale, spans both the biomedical engineering sciences and the field of surgical education. On a micro-scale the concepts of fractals, dimensions and material properties are harnessed to develop a simulator which is subsequently evaluated as a tool for the behaviorist, cognitivist and constructivist learning of a spectrum of epilepsy surgery techniques.The aim of the research was to develop a high fidelity cerebral hemispheric surgery simulator utilizing 3D printing- based technology and re-engineering the use of existing materials, to promote surgical education through hands-on-training of surgical epilepsy techniques. Leonardo da Vinci’s ingenuity of injecting hot wax into the cerebral ventricles of an ox is the earliest documentation of the use of a solidifying medium to recreate a phantom of an internal body organ. Over 500 years later similar techniques are still popularly used. This thesis describes ways of addressing this materials innovation gap. Over time the research project evolved from a focus on the novelty of designing a simulator to an emphasis on practical application of the technology through qualitative analysis. The thesis title: ‘The development of a cerebral hemispheric surgery simulator and its evaluation for epilepsy neurosurgical education’ encompasses both design and validation aspects and introduces the broad nature of epilepsy surgery procedures captured within this single simulator. The core methods consisted of two parts: the engineering process of the cerebral hemispheric surgery simulator and the evaluation of the invention for face, content, construct, and criterion validity. Ultimately, the results and conclusions are supportive of an invention that harnesses the strengths of biomedical engineering for the advancement of neurosurgical education.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.026
GPT teacher head0.347
Teacher spread0.321 · 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.

Study designOther design
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

Citations0
Published2022
Admission routes1
Has abstractyes

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