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Record W3126692631 · doi:10.1136/bmjgh-2020-004415

Availability and funding of clinical genomic sequencing globally

2021· review· en· W3126692631 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBMJ Global Health · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsUniversity of Calgary
FundersNational Human Genome Research InstituteNational Cancer InstituteNational Institute for Health and Care ResearchNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchIllumina
KeywordsExome sequencingGenomic sequencingGenetic testingPublic healthPrivate sectorEconomic growthMedicineBiologyGenomeGeneticsMutationPathology

Abstract

fetched live from OpenAlex

The emergence of next-generation genomic sequencing (NGS) tests for use in clinical care has generated widespread interest around the globe, but little is known about the availability and funding of these tests worldwide. We examined NGS availability across world regions and countries, with a particular focus on availability of three key NGS tests-Whole-Exome Sequencing or Whole-Genome Sequencing for diagnosis of suspected genetic diseases such as intellectual disability disorders or rare diseases, non-invasive prenatal testing for common genetic abnormalities in fetuses and tumor sequencing for therapy selection and monitoring of cancer treatment. We found that these NGS tests are available or becoming available in every major region of the world. This includes both high-income countries with robust genomic programmes such as the USA and the UK, and growing availability in countries with upper-middle-income economies. We used exploratory case studies across three diverse health care systems (publicly funded/national (UK), publicly funded/provincial (Canada) and mixed private/public system (USA)) to illustrate the funding challenges and approaches used to address those challenges that might be adopted by other countries. We conclude by assessing what type of data and initiatives will be needed to better track and understand the use of NGS around the world as such testing continues to expand.

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.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.134
GPT teacher head0.500
Teacher spread0.366 · 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