MétaCan
Menu
Back to cohort
Record W2136328158 · doi:10.1309/ajcpgfjp83ixzeur

Usefulness of Cytokeratin 5/6 and AMACR Applied as Double Sequential Immunostains for Diagnostic Assessment of Problematic Prostate Specimens

2009· article· en· W2136328158 on OpenAlexaff
Kiril Trpkov, Joanna Bartczak‐McKay, Aslı Yilmaz

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2009
Typearticle
Languageen
FieldMedicine
TopicUrologic and reproductive health conditions
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
Fundersnot available
KeywordsStainingPathologyCytokeratinImmunohistochemistryAtypiaProstate cancerProstateMedicineIntraepithelial neoplasiaCancerInternal medicine

Abstract

fetched live from OpenAlex

We evaluated the usefulness of double immunohistochemical staining for cytokeratin (CK)5/6 and alpha-methylacyl coenzyme A racemase (AMACR) applied sequentially on 1 slide by assessing 223 foci in 110 consecutive prostate specimens. Double-chromogen reaction was used to visualize the antibodies: brown for CK5/6 and red for AMACR. Staining was scored as diffuse, focal, or negative. To establish the diagnosis, CK5/6 and AMACR were correlated with the morphologic features. All cancers lacked CK5/6 staining (100% specificity). AMACR showed diffuse or focal positivity in cancer, high-grade prostatic intraepithelial neoplasia, and atypia in 96.8% (120/124), 85% (22/26), and 80% (16/20) of cases, respectively. In atypical cases, diagnosis was because of non-immunohistochemical staining reasons in 80% of cases. In adenosis (n = 14), AMACR was diffusely positive in 4 cases (29%). Double immunohistochemical staining for CK5/6 and AMACR is a simple assay to perform and may be used as an alternative to antibody cocktails for routine evaluation of problematic prostate specimens.

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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.061
GPT teacher head0.441
Teacher spread0.380 · 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

Citations38
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Clinical PathologySame topicUrologic and reproductive health conditionsFrench-language works237,207