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Record W1690432890 · doi:10.1016/j.ijcard.2015.05.140

Electronic medical record integration with a database for adult congenital heart disease: Early experience and progress in automating multicenter data collection

2015· article· en· W1690432890 on OpenAlexaff
Craig S. Broberg, Julie Mitchell, Silven Rehel, Andrew Grant, Ann Gianola, Peter Beninato, Christiane Winter, Amy Verstappen, Anne Marie Valente, Joseph B. Weiss, Ali N. Zaidi, Michael G. Earing, Stephen C. Cook, Curt J. Daniels, Gary D. Webb, Paul Khairy, Ariane Marelli, Michelle Gurvitz, David J. Sahn

Bibliographic record

VenueInternational Journal of Cardiology · 2015
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsMontreal Heart InstituteMcGill University Health CentreUniversité de MontréalUniversité de Sherbrooke
FundersNational Center for Advancing Translational SciencesNational Institutes of HealthNational Heart, Lung, and Blood InstituteGeorgia Clinical and Translational Science AllianceInstitute of Clinical and Translational Sciences
KeywordsMedicineData collectionMedical diagnosisCoding (social sciences)PopulationDatabaseScale (ratio)Data integrationData scienceComputer scienceStatisticsPathologyCartography

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
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 score0.070
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0000.000

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 head0.033
GPT teacher head0.360
Teacher spread0.327 · 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

Citations13
Published2015
Admission routes1
Has abstractno

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