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Record W2127718047 · doi:10.1136/jclinpath-2015-203452

Traditional serrated adenoma (TSA): morphological questions, queries and quandaries

2015· review· en· W2127718047 on OpenAlexaff
Runjan Chetty

Bibliographic record

VenueJournal of Clinical Pathology · 2015
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsAdenomaComputer scienceInformation retrievalComputational biologyPathologyData scienceMedicineBioinformaticsBiology

Abstract

fetched live from OpenAlex

AIM: Traditional serrated adenoma (TSA) is an uncommon type of serrated adenoma that can be a precursor to biologically aggressive colorectal cancer that invokes the serrated (accelerated) pathway. The purpose of this review is to address some of the more contentious issues around nomenclature, diagnostic criteria, histological variants, coexistence with other polyp types, the occurrence of dysplasia and the differential diagnosis. RESULTS: While the vast majority of TSAs are exophytic villiform polyps composed of deeply eosinophilic cells, flat top luminal serrations and numerous ectopic crypt foci, histological variants include flat TSA, filiform TSA and one composed of large numbers of mucin-containing cells. It is unlikely that there is any biological difference between the histological variants. There is a contention that TSAs are not dysplastic ab initio and that the majority do not show cytological atypia. Two types of dysplasia are associated with TSA. Serrated dysplasia is less well recognised and less commonly encountered than adenomatous dysplasia. TSA with dysplasia must be separated from TSA with coexisting conventional adenoma. CONCLUSIONS: TSA is a characteristic polyp that may be extremely exophytic, flat or composed of mucin-rich cells and is typified by numerous ectopic crypt foci. They may coexist with other serrated polyps and conventional adenomas. Approximately 20-25% will be accompanied by adenomatous dysplasia.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.356
GPT teacher head0.486
Teacher spread0.130 · 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 designOther design
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

Citations40
Published2015
Admission routes1
Has abstractyes

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