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Record W4220674548 · doi:10.1097/mou.0000000000000980

The role of radiotherapy in oligometastatic hormone-sensitive prostate cancer

2022· review· en· W4220674548 on OpenAlexaff
Nicholas D. James

Bibliographic record

VenueCurrent Opinion in Urology · 2022
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsMedicineSABR volatility modelProstate cancerRadiation therapyProstateOncologyHormonal therapyInternal medicineRadiologyCancer

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The aim of this article is to review the role of radiotherapy in the management of oligometastatic hormone-sensitive prostate cancer (HSPC). RECENT FINDINGS: The M1|RT STAMPEDE trial showed a survival advantage to prostate radiotherapy in newly diagnosed oligometastatic HSPC. The combination of prostate radiotherapy with systemic treatment is now the recommended standard of care. Metastases-directed therapy (MDT) with stereotactic ablative radiotherapy (SABR) in the STOMP and ORIOLE trial reported excellent local control and a survival advantage in metachronous oligometastatic HSPC. Results were consistent with prostate cancer outcomes in the SABR-COMET trial and the NHS England Commissioning through Evaluation scheme (CtE). SABR in synchronous oligometastatic HSPC will be evaluated in a new comparison within the STAMPEDE trial. Current definition of oligometastatic HSPC is based on the number of metastatic lesions on conventional imaging (CT/MRI and Isotope bone scan). Novel imaging, such as PSMA PET/CT provide superior accuracy to conventional imaging. However, limited data exists on the role of novel imaging in determining subsequent clinical outcomes. SUMMARY: Prostate radiotherapy improves survival and is standard of care with systemic treatment in newly diagnosed oligometastatic HSPC. The role of SABR in newly diagnosed oligometastatic HSPC is yet to be determined.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.994
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.401
Teacher spread0.335 · 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 teacher head, 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
Published2022
Admission routes1
Has abstractyes

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