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Record W1964666703 · doi:10.1109/tbme.2012.2230326

Real-Time Blood Circulation and Bleeding Model for Surgical Training

2012· article· en· W1964666703 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2012
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCauterizationBlood circulationSimple (philosophy)Computer scienceSurgerySimulationMedicineArtificial intelligenceRadiology

Abstract

fetched live from OpenAlex

Intraoperative management of bleeding is a critical skill all surgeons must possess. It is, however, very challenging to create a safe and realistic learning environment for its acquisition. In this paper, we propose a simple and efficient approach to integrate blood circulation to computerized surgical simulation systems and allow for real-time processing of punctures, ruptures, and cauterization of blood vessels. Blood pressures and flows are calculated using a system of ordinary differential equations, which can be simulated very efficiently. The equation system itself is constructed using a graph of the vessels' connectivity extracted from magnetic resonance angiograms (MRA) and completed with virtual vessels deduced from the principle of minimum work. Real-time performances of the method are assessed and results are demonstrated on ten patients who underwent a MRA before removal of a brain tumor.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score0.550

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 head0.024
GPT teacher head0.239
Teacher spread0.214 · 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