MétaCan
Menu
Back to cohort
Record W6958478165 · doi:10.6084/m9.figshare.19533984

Additional file 1 of Developing a random forest algorithm to identify patent foramen ovale and atrial septal defects in Ontario administrative databases

2022· article· en· W6958478165 on OpenAlexaffabout

Bibliographic record

VenueFigshare · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsInstitute for Clinical Evaluative SciencesCanadian Institute for Advanced ResearchUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsPatent foramen ovaleRandom forestBaseline (sea)Table (database)

Abstract

fetched live from OpenAlex

Additional file 1. Appendix A - Prior attempts in literature to differentiate PFO from ASD and other congenital heart diseases; Appendix B - Diagnostic and procedural codes used to define baseline comorbidities; Appendix C - Reproducible example with simulated data; Appendix D - Detailed table of baseline demographic information and comorbidities; Appendix E - Models tested to determine final classification algorithm.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.403

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.7180.079

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 head0.112
GPT teacher head0.312
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designSimulation or modeling
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 routes2
Has abstractyes

Explore more

Same venueFigshareSame topicCardiovascular and Diving-Related ComplicationsFrench-language works237,207