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Record W4392606713 · doi:10.1016/j.gimo.2024.101522

P616: Genome-wide Sequencing Ontario (GSO): Insight into Ontario’s rare disease landscape

2024· article· en· W4392606713 on OpenAlexaffabout
Meredith Gillespie, Robin Z. Hayeems, Christian R. Marshall, Anna Szuto, Caitlin Chisholm, Wendy J. Ungar, James Stavropoulos, Lijia Huang, Viji Venkataramanan, Lynette Lau, Wilson W. L. Sung, Mélanie Beaulieu Bergeron, Ted Higginbotham, Meredith Curtis, Venuja Sriretnakumar, Hassan Zaidi, E. Hitchcock, Audrey Schaffer, Sarah L. Sawyer, Gregory Costain, Roberto Mendoza‐Londono, Martin J. Somerville, Kym M. Boycott, Taila Hartley

Bibliographic record

VenueGenetics in Medicine Open · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsUniversity of TorontoInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick ChildrenChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsRare diseaseDiseaseGenomeGeographyBiologyGeneticsMedicineGeneInternal medicine

Abstract

fetched live from OpenAlex

Genome-wide sequencing (GWS) has been identified as a key strategy for achieving a timely diagnosis for patients with rare diseases. Prior to April 2021, access to all clinical GWS for Ontarians was facilitated by a Ministry of Health (MOH) out-of-country testing program. While this program provided access to testing not available at clinical laboratories in Canada, it was labor-intensive and did not provide knowledge of diagnostic outcomes from GWS. Establishment of local infrastructure was essential to increase access to GWS, enabling a locally representative knowledge base and a performance measurement system that could guide technical and policy decisions related to the use of GWS over time.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.001
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 head0.019
GPT teacher head0.274
Teacher spread0.254 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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