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Record W4410770345 · doi:10.1136/jmg-2025-110629

Canadian College of Medical Geneticists (CCMG) position statement on the storage of patient genetic and genomic information in electronic health records

2025· article· en· W4410770345 on OpenAlexaffabout
Anne‐Marie Laberge, Nolan D'Souza, Lynette S. Penney, Karim Jessa, Lauren Chad

Bibliographic record

VenueJournal of Medical Genetics · 2025
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsHospital for Sick ChildrenNova Scotia Health AuthorityIzaak Walton Killam Health CentreSickKids FoundationDalhousie UniversityUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsPosition statementScope (computer science)Position (finance)Statement (logic)Health recordsMedical recordWarrantData scienceState (computer science)Computer scienceInternet privacyMedicineHealth careFamily medicinePolitical scienceBusinessLaw

Abstract

fetched live from OpenAlex

The aim of this document is to provide an updated statement from the Canadian College of Medical Geneticists (CCMG) regarding the current state and some future considerations on the collection, distribution, and storage of genomic information within electronic health records (EHRs), including which aspects of genomic data might warrant special attention. The original version of this document was written by the CCMG Ethics and Public Policy committee in 2010 based on data collected via an online survey of the CCMG membership at the time. It is updated here to reflect the current state of healthcare in 2024, where EHRs are almost ubiquitously used, and genomic medicine has expanded in its breadth and scope. The document was circulated to the general membership for review and feedback and has been approved by the CCMG Board of Directors.

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)0.008
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.005
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 integrity0.0000.002
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 head0.065
GPT teacher head0.460
Teacher spread0.395 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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