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Introduction

2010· other· en· W4241375241 on OpenAlex
Leslie H. Sobin, M.K. Gospodarowicz, Ch. Wittekind

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

VenueTNM Online · 2010
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCategorizationCancerMedicineTNM staging systemMedical physicsOncologyInternal medicineNeoplasm stagingComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The TNM system is the most widely used means for classifying the extent of cancer spread. TNM Classification of Malignant Tumours, Sixth Edition provides the new, internationally agreed‐upon standards to describe and categorize cancer stages and progression. Published in affiliation with the International Union Against Cancer (UICC), this guide contains important new and updated organ‐specific classifications that oncologists and other professionals who treat patients with cancer must use to adequately classify tumours for prognosis and treatment. This introduction provides a history of the TNM system, the principles of the classification of cancers and general rules of the TNM system applicable to all sites. Headings used in the TNM system to classify tumours for specific anatomical regions and sites are also provided with definitions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.025
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0060.000

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 head0.005
GPT teacher head0.241
Teacher spread0.237 · 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