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Record W2518498353 · doi:10.21873/anticanres.10994

The Utility of Serum CA9 for Prognostication in Prostate Cancer

2016· article· en· W2518498353 on OpenAlexafffundabout
Alan D. Smith, Robert G. Bristow, Paul S. F. Yip, Michael Milosevic, Anthony M. Joshua

Bibliographic record

VenueAnticancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
FundersPrincess Margaret Cancer Foundation
KeywordsProstate cancerProstateMedicineCancerProstate diseasePathologyOncologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND/AIM: Carbonic anhydrase IX (CA9) catalyses the interconversion of carbon dioxide to carbonic acid and bicarbonate and is considered a putative biomarker of tumour hypoxia. We set out to evaluate the prognostic significance of CA9 in prostate cancer. PATIENTS AND METHODS: Plasma samples were assessed from 68 men with high-risk localised prostate cancer treated with radical prostatectomy (RP) or radiotherapy (RT), and 20 men with castration-resistant prostate cancer (CRPC) treated with docetaxel chemotherapy between 2010 and 2012 at the Princess Margaret Cancer Centre, Canada. RESULTS: Of the 68 patients with high-risk localised prostate cancer, 57 underwent RP and 11 underwent RT. Baseline CA9 was not associated with recurrence or prostate-specific antigen in either group (p=0.98 and 0.20, respectively). CA9 levels before chemotherapy correlated with overall survival (r=-0.37; two-sided p=0.11). CONCLUSION: Baseline CA9 in men with CRPC may portend a more aggressive prostate cancer phenotype with poorer survival.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.056
GPT teacher head0.411
Teacher spread0.355 · 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

Citations16
Published2016
Admission routes3
Has abstractyes

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