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Record W4249874770 · doi:10.1002/9781118868126.ch7

Active Surveillance for Low‐Risk Prostate Cancer

2017· other· en· W4249874770 on OpenAlexaff
Laurence Klotz

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineProstate cancerConservative managementDiseaseMagnetic resonance imagingWatchful waitingIntensive care medicineCancerBiomarkerConservative treatmentRisk assessmentInternal medicineOncologySurgeryRadiology

Abstract

fetched live from OpenAlex

Low-risk and many cases of low-intermediate risk prostate cancer have little or no metastatic potential and do not pose a threat to the patient in his lifetime. Substantial recent evidence has clarified who these patients are and supported the use of conservative management in such individuals. A key element of conservative management is the early identification of those low-risk patients who harbor higher-risk disease and benefit from definitive therapy. This represents about 30% of newly diagnosed low-risk patients. A further small proportion of patients with low-risk disease demonstrate biological progression over time to higher grade disease. Men with lower-risk disease can defer treatment, in most cases, for life. The results of active surveillance—embodying conservative management with selective delayed intervention for the subset who are reclassified as higher risk over time based on repeat biopsy, imaging, or biomarker results—have shown that this approach is safe in the intermediate to long term, with a 5% cancer specific mortality at 15 years. Further refinement of the surveillance approach is ongoing, incorporating magnetic resonance imaging (MRI), targeted biopsies, and molecular biomarkers.

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)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.002

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 head0.026
GPT teacher head0.375
Teacher spread0.349 · 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
GenreOther

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

Citations0
Published2017
Admission routes1
Has abstractyes

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