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Record W2981630704 · doi:10.1111/bju.14935

Active surveillance in intermediate‐risk prostate cancer

2019· article· en· W2981630704 on OpenAlexaff
Laurence Klotz

Bibliographic record

VenueBritish Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineProstate cancerDiseaseProstatePopulationInternal medicineCancerMagnetic resonance imagingRadiological weaponBiopsyGenetic testingOncologySurgeryRadiology

Abstract

fetched live from OpenAlex

OBJECTIVES: Active surveillance (AS), now the standard of care for most men with favourable-risk prostate cancer, is appealing for selected men with 'favourable' intermediate-risk prostate cancer. METHODS: This is a review of the indications for conservative management in this population, the outcomes reported in prospective series, and the use of molecular biomarkers and imaging to identify optimal candidates. RESULTS: Candidates are those patients who are categorized as having intermediate-risk disease either because of a prostate-specific antigen level between 10 and 20 ng/mL, or by virtue of having Grade Group 2 disease, with a small percentage of Gleason 4 pattern, and a negative magnetic resonance imaging result or negative targeted biopsy of a region of interest. Confirmation with a favourable score on a tissue-based genetic assay can provide further reassurance. A subset of patients with intermediate-risk disease has indolent disease that may benefit from AS; at the same time, some patients with intermediate-risk disease have an aggressive clinical course that requires early definitive therapy. This heterogeneity is not adequately captured with traditional histopathological staging. Clinical, genomic and radiological biomarkers are the key to appropriate risk stratification and patient selection. CONCLUSIONS: The benefits of AS make it an appealing option for selected patients with intermediate-risk disease.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.260
Teacher spread0.253 · 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 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

Citations88
Published2019
Admission routes1
Has abstractyes

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