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Record W2151059881 · doi:10.1136/jcp.2005.026906

CD44s is useful in the differentiation of benign and malignant papillary lesions of the breast

2005· article· en· W2151059881 on OpenAlexaff
Gary M. Tse, P-H Tan, T K F, C. Blake Gilks, Chi Sun Poon, Bonita K.B. Law

Bibliographic record

VenueJournal of Clinical Pathology · 2005
Typearticle
Languageen
FieldMedicine
TopicBreast Lesions and Carcinomas
Canadian institutionsVancouver General Hospital
Fundersnot available
KeywordsIntraductal papillomaMyoepithelial cellPathologyPapillomaImmunohistochemistryMedicineMalignant transformationDuctal carcinomaPapillary carcinomaStainingCarcinomaBreast cancerCancerInternal medicineThyroid

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: CD44s, the standard form of CD44, has been shown to be downregulated during malignant transformation of breast cancers. It has also been reported recently to be a useful marker in differentiating between benign and malignant papillary lesions of the breast, with high expression in the former. CD44s expression in benign and malignant papillary lesions was evaluated. METHODS: CD44s expression was assessed by immunohistochemistry in 101 benign papillomas and 59 papillary carcinomas (seven invasive papillary carcinomas, 41 papillary ductal carcinomas in situ, and 11 ductal carcinomas involving papillomas). RESULTS: Patients' age and tumour size were significantly different between the papilloma and papillary carcinoma groups (p < 0.0001). CD44s showed positive staining in 45 papillomas (45%) and five papillary carcinomas (8%), and the difference was significant (p < 0.0001). The myoepithelial cells, when present, were also positive for CD44s in both groups, with no observable differences. Using CD44s positive staining to differentiate between benign and malignant papillary lesions gives a sensitivity, specificity, and accuracy of 45%, 92%, and 62%, respectively. CONCLUSIONS: CD44s may be useful as an adjunct in the evaluation of morphologically problematic cases of papillary lesion of the breast.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.179

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.054
GPT teacher head0.352
Teacher spread0.298 · 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 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

Citations33
Published2005
Admission routes1
Has abstractyes

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