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Record W4200163190 · doi:10.3389/fonc.2021.780507

Drug Intensification in Future Postoperative Radiotherapy Practice in Biochemically-Relapsing Prostate Cancer Patients

2021· article· en· W4200163190 on OpenAlexaff
Axel Cailleteau, Paul Sargos, Fred Saad, I. Latorzeff, S. Supiot

Bibliographic record

VenueFrontiers in Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersJanssen PharmaceuticalsIpsenAstellas PharmaAstraZeneca
KeywordsProstate cancerMedicineProstatectomyRadiation therapyProstateOncologySalvage therapyInternal medicineAndrogen receptorCancerUrologyChemotherapy

Abstract

fetched live from OpenAlex

Although salvage prostate bed radiotherapy is highly effective in biochemically-relapsing prostate cancer patients following prostatectomy, relapses remain frequent and improvements are needed. Randomized phase 3 trials have shown the benefit of adding androgen-depriving therapy to irradiation, but not all patients benefit from this combination. Preclinical studies have shown that novel agents targeting the androgen receptor, DNA repair, PI3K/AKT/mTOR pathways, or the hypoxic microenvironment may help increase the response to prostate bed irradiation while minimizing potential side effects. This perspective review focuses on the most relevant molecules that may have an impact when combined with salvage radiotherapy, and underlines the strategies that need to be developed to increase the efficacy of salvage post-prostatectomy radiotherapy in prostate cancer patients.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.010
GPT teacher head0.339
Teacher spread0.330 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in OncologySame topicProstate Cancer Treatment and ResearchFrench-language works237,207