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Record W3208960913 · doi:10.1177/17588359211051870

Should androgen deprivation therapy and other systemic treatments be used in men with prostate cancer and a rising PSA post-local treatments?

2021· review· en· W3208960913 on OpenAlexaff
Anna Patrikidou, Thomas Zilli, Giulia Baciarello, Safae Terisse, Zineb Hamilou, Karim Fizazi

Bibliographic record

VenueTherapeutic Advances in Medical Oncology · 2021
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineProstate cancerAndrogen deprivation therapyOncologySystemic therapyHormone therapyInternal medicineClinical trialRadiation therapyProstate-specific antigenDiseaseCancerBreast cancer

Abstract

fetched live from OpenAlex

Biochemical recurrence is an evolving space in prostate cancer, with increasing multidisciplinary involvement. Androgen deprivation therapy has shown proof of its value in complementing salvage radiotherapy in high-risk biochemical relapsing patients; ongoing trials aim to further refine this treatment combination. As systemic treatments, and notably next-generation androgen receptor targeted agents, have moved towards early hormone-sensitive and non-metastatic stages, the prostate specific antigen (PSA)-relapse disease stage will be undoubtedly challenged by future evidence from such ongoing clinical trials. With the use of modern imaging and newer molecular technologies, including integration of tumoral genomic profiling and liquid biopsies in risk stratification, a path towards a precision oncology-focused approach will become a reality to guide in the future decisions for patients with a diagnosis of biochemical recurrence.

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)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.102
GPT teacher head0.460
Teacher spread0.358 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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