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Record W2782671629 · doi:10.1093/ajcp/aqx118.100

101 Teaching Next-Generation Sequencing Analysis to the Next Generation of Genetics Technologists—The Effective Delivery of a Hands-On Analysis Workshop

2018· article· en· W2782671629 on OpenAlexaffabout

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsWorkflowAccreditationMedical geneticsMedical educationMassive parallel sequencingDNA sequencingComputer scienceData scienceMedicineGeneticsBiology

Abstract

fetched live from OpenAlex

The latest competency profile for students enrolled in Canadian Medical Association-accredited clinical genetics technology programs, effective 2015, include the understanding of next-generation sequencing (NGS) technology. NGS is a form of massively parallel sequencing, and the implementation of NGS has advanced rapidly in clinical genetics. Many health care institutions have introduced NGS workflows to deliver multi-gene test results. This has created a need to teach technical and analytical components to student technologists as they embark on a career where demand for NGS testing will substantially increase. The Advanced Molecular Diagnostics Laboratory (AMDL) located in the Princess Margaret Cancer Centre designed a half-day workshop on NGS analysis for genetics technology students at the Michener Institute, which offers one of two accredited clinical genetics programs in Canada. The application of NGS testing has become multidisciplinary; technologists have had to adapt to working with bioinformaticians and variant annotation specialists, and learn a multitude of laboratory skills in between. Program instructors at the Michener Institute decided to showcase the evolving role of genetics technologists by inviting AMDL to deliver a workshop focused on (1) data generation from the sequencer, (2) the use of genetic software, and (3) the basic understanding of variant detection and clinical implications. The workshop concluded with provision of examples for students to practice variant calling, reflecting real case scenarios from technologists’ perspective. This is the second year AMDL presented to students; feedback confirmed strong interest in gaining practical exposure to NGS applications, and identified needed improvements to bridge gaps between teaching the use of advanced software and manual sequencing analysis. Student evaluations further reflect usefulness in reviewing NGS data types, but equal interest to learn fundamentals of variant interpretation. The overall reception supports a prerequisite for new technologists to embrace clinical NGS.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.116
GPT teacher head0.395
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2018
Admission routes2
Has abstractyes

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