MétaCan
Menu
Back to cohort
Record W2154023152 · doi:10.1109/iembs.1999.804406

Methodologies for predicting coronary surgery outcomes

2003· article· en· W2154023152 on OpenAlexafffund
Colleen M. Ennett, Monique Frize, R Shaw

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicArtificial Intelligence in Healthcare
Canadian institutionsCarleton UniversityUniversity of Ottawa
FundersMedical Research CouncilNatural Sciences and Engineering Research Council of CanadaMedical Research Council Canada
KeywordsBypass graftingComputer scienceArtificial neural networkRepresentation (politics)ArteryBinary numberGraftingArtificial intelligenceDatabaseCardiologyMedicineMathematicsMaterials science

Abstract

fetched live from OpenAlex

Preliminary results using an artificial neural network (ANN) on a coronary artery bypass grafting (CABG) surgery database highlighted challenges when faced with a low representation of a binary variable in the database. We will artificially alter the distribution of the database by reproducing or removing cases, and observe any changes in ANN performance. Final results will be presented at the conference.

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)0.004
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.659
GPT teacher head0.598
Teacher spread0.060 · 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 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

Citations7
Published2003
Admission routes2
Has abstractyes

Explore more

Same topicArtificial Intelligence in HealthcareFrench-language works237,207