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Record W4392606786 · doi:10.1016/j.gimo.2024.101439

P540: Genome-wide Sequencing Ontario (GSO): Canada’s first provincial clinical genome-wide sequencing service

2024· article· en· W4392606786 on OpenAlexaffabout
Meredith Gillespie, Robin Z. Hayeems, Christian R. Marshall, Anna Szuto, Caitlin Chisholm, James Stavropoulos, Lijia Huang, Lynette Lau, Wilson W. L. Sung, Mélanie Beaulieu Bergeron, Ted Higginbotham, Meredith Curtis, Venuja Sriretnakumar, Hassan Zaidi, E. Hitchcock, Audrey Schaffer, Taila Hartley, Sarah L. Sawyer, Wendy J. Ungar, Gregory Costain, Roberto Mendoza‐Londono, Anna Pan, Jennifer L. Keating, Diana Matviychuk, Tamara Braid, Martin J. Somerville, Kym M. Boycott

Bibliographic record

VenueGenetics in Medicine Open · 2024
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsAgricultural Research Institute of OntarioSickKids FoundationUniversity of TorontoChildren's Hospital of Eastern OntarioInstitute for Clinical Evaluative SciencesHospital for Sick Children
Fundersnot available
KeywordsDNA sequencingGenomeCancer genome sequencingBiologyWhole genome sequencingComputational biologyGeneticsGene

Abstract

fetched live from OpenAlex

In Ontario, the most populated province in Canada (15M), access to clinical exome sequencing (ES) has historically been facilitated by a Ministry of Health (MOH) out-of-country exceptional access program. This process presented challenges to the healthcare system, namely, such as a burdensome funding approval process for clinical providers which added several months to patients’ diagnostic journeys, overall test performance was unknown, genome-wide sequencing (GWS) data was lost for future reanalysis or data sharing, healthcare dollars were spent outside of the country, and there remained uncertainty regarding the most appropriate approach to GWS.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.006

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 head0.264
GPT teacher head0.456
Teacher spread0.193 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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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