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Record W577600429

Risks, benefits, and approaches to hormonal blockade in prostate cancer. Highlights from the European Association of Urology Meeting, March 20-24, 2015, Madrid, Spain.

2015· other· en· W577600429 on OpenAlexaff
Jack Barkin

Bibliographic record

VenuePubMed · 2015
Typeother
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsHumber River Regional Hospital
Fundersnot available
KeywordsMedicineProstate cancerHormone antagonistAgonistGonadotropin-releasing hormonePrednisoneUrologyAndrogen deprivation therapyHormoneInternal medicineIncidence (geometry)Adverse effectBlockadeAbiraterone acetateTestosterone (patch)Luteinizing hormoneOncologyEndocrinologyCancerReceptorEndocrine system
DOInot available

Abstract

fetched live from OpenAlex

Several abstracts presented at the 2015 European Association of Urology Meeting highlighted new developments in hormone therapy for prostate cancer management. One abstract described how the luteinizing hormone-releasing hormone (LHRH)/gonadotropin-releasing hormone (GnRH) agonist leuprolide, but not the LHRH/GnRH antagonist degarelix, induced plaque instability in a mouse model. A second abstract showed that in patients with a history of severe cardiovascular disease, degarelix was associated with fewer cardiovascular events than treatment with an LHRH agonist. A third abstract showed how primary androgen-deprivation therapy was linked with increased all-cause mortality in a US registry. A fourth abstract showed that in the ANAMEN study, cognitive performance was not significantly affected by 6 months of treatment with GnRH agonists. Last, a fifth abstract showed that low-dose prednisone, with or without abiraterone, was associated with an overall low incidence of corticosteroid-associated adverse events.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.002

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 head0.086
GPT teacher head0.275
Teacher spread0.189 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuePubMed→Same topicProstate Cancer Treatment and Research→French-language works237,207→