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Confirmation of ProMisE: A simple, genomics‐based clinical classifier for endometrial cancer

2017· article· en· 960 citations· W2570729870 on OpenAlex· 10.1002/cncr.30496

Why is this work in the frame?

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

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.
Canadian funderA Canadian agency funded it. The work may carry no Canadian affiliation at all.

Full frame distilled prediction

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.

Candidate categories
Insufficient payload (model declined to judge)
Consensus categories
none
Domain
Candidate signal: noneConsensus signal: none
Study design
Candidate signal: ObservationalConsensus signal: none
Genre
Candidate signal: EmpiricalConsensus signal: Empirical
Teacher disagreement score
0.590
Threshold uncertainty score
1.000
Validation status
machine_predicted_unvalidated · codex-gemma-dda1882f352a

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

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.

Opus teacher head0.202
GPT teacher head0.475
Teacher spread
0.273 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

BACKGROUND: Classification of endometrial carcinomas (ECs) by morphologic features is irreproducible and imperfectly reflects tumor biology. The authors developed the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE), a molecular classification system based on The Cancer Genome Atlas genomic subgroups, and sought to confirm both feasibility and prognostic ability in a new, large cohort of ECs. METHODS: Immunohistochemistry (IHC) for the presence or absence of mismatch repair (MMR) proteins (to identify MMR deficiency [MMR-D]), sequencing for polymerase-ɛ (POLE) exonuclease domain mutations (POLE EDMs), and IHC for tumor protein 53 (p53) (wild type vs null/missense mutations; p53 wt and p53 abn, respectively) were performed on 319 new EC samples. Subgroups were characterized and assessed relative to outcomes. The prognostic ability of ProMisE was compared with that of current risk-stratification systems (European Society of Medical Oncology [ESMO]). RESULTS: ProMisE decision-tree classification achieved categorization of all cases and identified 4 prognostic subgroups with distinct overall, disease-specific, and progression-free survival (P < .001). Tumors with POLE EDMs had the most favorable prognosis, and those with p53 abn the worst prognosis, and separation of the 2 middle survival curves (p53 wt and MMR-D) was observed. There were no significant differences in survival between the ESMO low-risk and intermediate-risk groups. ProMisE improved the ability to discriminate outcomes compared with ESMO risk stratification. There was substantial overlap (89%) between the p53 abn and high-risk ESMO subgroups; but, otherwise, there were no predictable associations between molecular and ESMO risk groups. CONCLUSIONS: Molecular classification of ECs can be achieved using clinically applicable methods and provides independent prognostic information beyond established clinicopathologic risk factors available at diagnosis. Consistent, biologically relevant categorization enables stratification for clinical trials and/or targeted therapy, identification of women who are at increased risk of having Lynch syndrome, and may guide clinical management. Cancer 2017;123:802-13. © 2016 American Cancer Society.

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.

The record

Venue
Cancer
Topic
Endometrial and Cervical Cancer Treatments
Field
Medicine
Canadian institutions
Vancouver General HospitalMcGill University Health CentreMcGill UniversityUniversity Health NetworkUniversity of British ColumbiaBC Cancer Agency
Funders
Canadian Institutes of Health ResearchBC Cancer Foundation
Keywords
MedicineOncologyEndometrial cancerDNA mismatch repairInternal medicineLynch syndromeCancerBioinformaticsColorectal cancerBiology
Has abstract in OpenAlex
yes