MétaCan
Menu
Back to cohort
Record W3102539560 · doi:10.1136/jmedgenet-2020-107394

Canadian College of Medical Geneticists (CCMG) points to consider: resuming genetic services in a pandemic—a summary

2020· article· en· W3102539560 on OpenAlexaffabout
Lauren Chad, Angelika J. Dawson, Elaine Goh

Bibliographic record

VenueJournal of Medical Genetics · 2020
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsTrillium Health CentreManitoba HealthHospital for Sick Children
Fundersnot available
KeywordsContext (archaeology)PandemicStakeholderHealth careMedicineGenetic counselingFamily medicineBusinessPublic relationsCoronavirus disease 2019 (COVID-19)Political scienceGeography

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has disrupted the provision of genetic care in Canada. With the public health effort to flatten the curve, many clinics have moved to virtual care for select populations of patients while triaging and postponing others. As genetic services are asked to gradually resume, a roadmap is needed to ensure clinical care decisions for at-risk patients are transparent and equitable, that postponed care is resumed and that patients with or waiting for a genetic diagnosis are not disproportionately affected or abandoned.The purpose of this document is to highlight the guiding ethical principles and stakeholder considerations in resuming genetic services to help guide the competing needs going forward of both limiting exposures while maintaining high-quality care. Considerations highlighted are (1) environment of practice, (2) nature of consult, (3) patient factors, (4) provider factors, and (5) laboratory factors. The intended users are those providing genetic care in a Canadian context with the recognition that there are clinic-specific and regional variations that will influence decision-making. While specific to the Canadian context, the ethical principles used to guide these decisions would be relevant for consideration in other jurisdictions.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.349
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.038
GPT teacher head0.334
Teacher spread0.296 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Medical GeneticsSame topicBiomedical Ethics and RegulationFrench-language works237,207