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Record W1538845011 · doi:10.1111/iju.12069

Radical prostatectomy in high‐risk prostate cancer

2013· review· en· W1538845011 on OpenAlexaff
Joseph Ischia, Martin Gleave

Bibliographic record

VenueInternational Journal of Urology · 2013
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsVancouver General Hospital
Fundersnot available
KeywordsMedicineProstatectomyProstate cancerAndrogen deprivation therapyProstate-specific antigenRadiation therapyRandomized controlled trialUrologyDiseaseProstateCancerSurgeryInternal medicine

Abstract

fetched live from OpenAlex

One consistent finding in the studies regarding treating men with prostate cancer is that men with high-risk disease have the most to gain from treatment with curative intent. Men with high-risk or locally-advanced prostate cancer require treatment to the primary cancer or risk dying prematurely from their disease. Increasingly, combined androgen deprivation therapy + radiation treatment is seen as the standard treatment as a result of prospective studies in this space, and the perceived increased morbidity of radical prostatectomy in the setting of a "low" cure rate as monotherapy. In the absence of a well-conducted randomized trial, there is no definite evidence that one treatment is superior to the other. The advantages of radical prostatectomy are that it provides excellent local control of the primary tumor without an increase in morbidity, accurately stages the disease to guide further therapy, and removes benign sources of prostate-specific antigen so that failures can be promptly identified and subsequent treatment can be initiated in a timely manner. Although several guidelines recommend radiation treatment over radical prostatectomy as first-line treatment, there is no evidence that surgery is inferior and radical prostatectomy should remain part of any informed discussion regarding treatment options for men with high-risk prostate cancer.

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 score0.988
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.363
Teacher spread0.337 · 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

Citations24
Published2013
Admission routes1
Has abstractyes

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