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Record W2997467763 · doi:10.3747/co.26.5281

The Somatic Curation and Interpretation Across Laboratories (SOCIAL) Project—Current State of Solid-Tumour Variant Interpretation for Molecular Pathology in Canada

2019· article· en· W2997467763 on OpenAlexaffvenueabout
Tara Spence, Mahadeo A. Sukhai, Suzanne Kamel‐Reid, Tracy Stockley

Bibliographic record

VenueCurrent Oncology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsInterpretation (philosophy)MedicinePathologyMolecular pathologySomatic cellComputational biologyData scienceBioinformaticsBiologyComputer scienceGeneticsGeneProgramming language

Abstract

fetched live from OpenAlex

Background: Practices in somatic variant interpretation and classification vary between Canadian clinical molecular diagnostic laboratories, and understanding of current practices and perspectives is limited. To define gaps and future directions, including consensus guideline development, the Somatic Curation and Interpretation Across Laboratories (social) project examined the present state of somatic variant interpretation in Canadian molecular laboratories, including testing volumes and methods, data sources and evidence criteria, and application of published classification guidelines. Methods: Individuals who perform somatic variant interpretation in Canadian centres were invited to participate in an online survey. Invitees included laboratory directors (certified as Fellows of the Canadian College of Medical Geneticists or the American College of Medical Geneticists), md or md and phd molecular pathologists, and other phd experts, including phd specialists in variant annotation or bioinformatics. Current testing methods, volumes, and platforms in next-generation sequencing, use of variant annotation resources and evidence criteria, and preference for variant classification schemes were evaluated. Results: Responses were received from 37 participants in 8 provinces. A somatic variant classification scheme jointly supported by the Association for Molecular Pathology (amp), the American Society of Clinical Oncology (asco), and the College of American Pathologists (cap) was used by 47% of respondents; an alternative guideline or a combination of published guidelines was used by 35% of respondents. The remaining 18% did not use a published scheme. Only 41% of respondents used a published scheme without alteration. Although all respondents indicated that there is a need for Canadian laboratories to adopt a somatic variant classification guideline, only 38% of respondents felt that it should be mandatory to adopt the amp/asco/cap-endorsed guideline. Conclusions: Data from the social project identified high variability in current practice, yet strong support for standardization of solid-tumour somatic variant interpretation across Canadian institutions. Aligning classification methods will reduce variation in cross-institutional classification and reporting practices, aiding in consistent practice nationwide.

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)0.036
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.836
Threshold uncertainty score0.970

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.011
Science and technology studies0.0080.005
Scholarly communication0.0070.002
Open science0.0050.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.346
Teacher spread0.331 · 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 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

Citations7
Published2019
Admission routes3
Has abstractyes

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