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Record W2897566333 · doi:10.1002/gcc.22690

Targeted RNA sequencing: A routine ancillary technique in the diagnosis of bone and soft tissue neoplasms

2018· review· en· W2897566333 on OpenAlex
Brendan C. Dickson, David Swanson

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.

Bibliographic record

VenueGenes Chromosomes and Cancer · 2018
Typereview
Languageen
FieldMedicine
TopicMedical Imaging and Pathology Studies
Canadian institutionsSinai Health SystemLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
Fundersnot available
KeywordsMedicineDNA sequencingSoft tissuePathologyComputational biologyBiologyGeneGenetics

Abstract

fetched live from OpenAlex

The past decade has witnessed remarkable progress in delineating the molecular pathogenesis of many mesenchymal neoplasms. This, in large part, is attributable to the application of next-generation sequencing. As these techniques decrease in cost, and increasingly support the use of routine clinical specimens-such as formalin-fixed paraffin-embedded tissue and cytology samples-they are beginning to be routinely implemented in diagnostic pathology laboratories. The breadth of testing possible by next-generation sequencing makes this a useful adjunct for pathologists, particularly with the emergence of targeted therapies. The intent of this article is to share our experience, over 2 years, as an early adopter of targeted RNA sequencing as an ancillary diagnostic technique for fusion gene detection in bone and soft tissue neoplasms.

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 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 score0.978
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.062
GPT teacher head0.356
Teacher spread0.295 · 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