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Correlation between the DCIS Score and traditional clinicopathologic features in the prospectively-designed Ontario population-based validation study.

2015· article· en· W2602658251 on OpenAlexaffabout
Eileen Rakovitch, Sharon Nofech‐Mozes, Wedad Hanna, Frederick L. Baehner, Refik Saskin, Steven M. Butler, Alan B. Tuck, Sandip Sengupta, Leela Elavathil, Prashant Jani, M. Bonin, Martin C. Chang, Susan J. Robertson, Elzbieta Slodkowska, Cindy Fong, Joseph M Anderson, Farid Jamshidian, Diana B. Cherbavaz, Steven Shak, Lawrence Paszat

Bibliographic record

VenueJournal of Clinical Oncology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsThunder Bay Regional Health Sciences CentreMount Sinai HospitalKingston General HospitalJuravinski HospitalQueen's UniversityLondon Health Sciences CentreHealth Sciences NorthUniversity of TorontoOttawa HospitalHealth Sciences CentreInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineComedoInternal medicinePopulationDuctal carcinomaBreast cancerCorrelationOncologyCancer

Abstract

fetched live from OpenAlex

581 Background: In the Ontario population based study, the DCIS Score was significantly associated with 10 year risk of an ipsilateral local recurrence (LR - in situ or invasive carcinoma) in women treated with breast conserving surgery (BCS) without radiation (RT) (P < 0.001). Here we evaluate correlation between DCIS Score and clinicopathologic (CP) features in the same cohort, and whether DCIS Score provides independent recurrence risk information. Methods: The study population included 571 women diagnosed with DCIS in the province of Ontario from 1994 – 2003 prospectively selected for treatment with BCS without RT. CP variables examined included age at diagnosis, DCIS tumor size, DCIS nuclear grade, comedo necrosis (absent, focal, or extensive), histologic type, multifocality, and surgical margin width. The association between DCIS Score and CP variables was examined by spearman rank correlation, and proportional hazards regression models were used to determine variables significantly associated with LR. Results: Tumor size (p = 0.002), multifocality (p < 0.001), histologic type (p = 0.005), and nuclear grade (p = 0.04) were significantly associated with LR. In a multivariable analysis, including significant CP covariates, the DCIS Score was statistically significantly associated with LR (p = 0.02). DCIS Score was moderately correlated with grade (rs= 0.47; 95% CI 0.41,0.54), comedo necrosis (rs= 0.43; CI 0.36,0.50), tumor size (rs= 0.24; CI 0.13,0.35), and multifocality (rs= 0.11; CI 0.03,0.19) but not other features. All CP subgroups showed a wide range of DCIS Scores in each subgroup. Conclusions: DCIS Score is only moderately correlated with grade, comedo necrosis, tumor size, and multifocality. DCIS Score provides recurrence risk information independent of CP features, and quantifies risk of local recurrence in individuals treated by BCS alone, validating previous findings from the E5194 clinical trial

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.635

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.178
GPT teacher head0.418
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2015
Admission routes2
Has abstractyes

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