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Record W4388076903 · doi:10.3390/curroncol30110700

Future Role of Health Technology Assessment for Genomic Medicine in Oncology: A Canadian Laboratory Perspective

2023· review· en· W4388076903 on OpenAlexaffvenueabout
Don Husereau, Yvonne Bombard, Tracy Stockley, Michael D. Carter, Scott Davey, Diana Lemaire, Erik Nohr, Paul Park, Alan Spatz, Christine Williams, Aaron Pollett, Bernard Lo, Stephen Yip, Soufiane El Hallani, Harriet Feilotter

Bibliographic record

VenueCurrent Oncology · 2023
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of AlbertaUniversity of British ColumbiaSinai Health SystemUniversity of CalgaryCanada Research ChairsQueen's UniversityOntario Institute for Cancer ResearchNova Scotia Health AuthorityUniversity of ManitobaFoothills Medical CentreOttawa HospitalUniversity Health NetworkUniversity of TorontoJewish General HospitalSt. Michael's HospitalMcGill University Health CentreUniversity of Ottawa
Fundersnot available
KeywordsMedicineGenomic medicinePersonalized medicinePrecision medicineHealth carePerspective (graphical)OncologyInternal medicineComputational biologyBioinformaticsPathologyBiologyComputer scienceArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

Genome-based testing in oncology is a rapidly expanding area of health care that is the basis of the emerging area of precision medicine. The efficient and considered adoption of novel genomic medicine testing is hampered in Canada by the fragmented nature of health care oversight as well as by lack of clear and transparent processes to support rapid evaluation, assessment, and implementation of genomic tests. This article provides an overview of some key barriers and proposes approaches to addressing these challenges as a potential pathway to developing a national approach to genomic medicine in oncology.

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)0.021
metaresearch head score (Gemma)0.033
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score0.938

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0070.011
Science and technology studies0.0010.006
Scholarly communication0.0060.005
Open science0.0030.002
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0060.001

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 head0.601
GPT teacher head0.606
Teacher spread0.005 · 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
GenreReview

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

Citations5
Published2023
Admission routes3
Has abstractyes

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