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Record W2804292967 · doi:10.17116/oftalma201882259-64

A technique of manufacturing anatomical preparations of the human brain based on injecting vessels with colored silicone (a technical note)

2018· article· en· W2804292967 on OpenAlexaff
M A Shkarubo, G F Dobrovol'skiy, G. A. Polev, A N Shkarubo, Г С Тархнишвили, L. I. Spitsyna, V V Karnaukhov, Andrey Bykanov

Bibliographic record

VenueBurdenko s Journal of Neurosurgery · 2018
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsInternal carotid arterySiliconeInternal jugular veinSkullCadaverAnatomyMedicineSilicone rubberVeinBiomedical engineeringMaterials scienceSurgeryComposite material

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective was to produce anatomical preparations by injecting vessels with colored silicone to study the brain and skull base anatomy. MATERIAL AND METHODS: Fresh, undissected, and unfixed cadavers were used. The internal carotid arteries and internal jugular veins were identified on both sides of the neck. The vessels were washed with running water. Then, a complex solution consisting of white silicone rubber, silicone oil (solvent), and a coloring pigment (red and blue pigments) at a ratio of 1:(0.9-1.1):(0.04-0.06), respectively, was prepared. About 30-60 s before injecting the complex solution into the vessels, a catalyst-hardener was added to the solution at a ratio of 1:(0.05-0.07). The complex solution was first introduced into the internal carotid artery until the solution came out from the contralateral internal carotid artery; then, the solution was injected into the internal jugular vein until the solution emerged from the contralateral internal jugular vein. RESULTS: The technique enables quick and high-quality visualization of both large and very small vessels of the brain and skull base. CONCLUSION: The proposed simple and inexpensive technique of manufacturing anatomical preparations improves the quality of training and mastering of microsurgical skills in residents and practicing neurosurgeons.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.400

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 integrity0.0000.001
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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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 designBench or experimental
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

Citations5
Published2018
Admission routes1
Has abstractyes

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