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Record W1985630081 · doi:10.1002/cncr.20726

Society of Urologic Oncology position statement: Redefining the management of hormone‐refractory prostate carcinoma

2004· article· en· W1985630081 on OpenAlexaff
Sam S. Chang, Mitchell C. Benson, Steven C. Campbell, Juanita Crook, Robert Dreicer, Christopher P. Evans, M. Craig Hall, Celestia S. Higano, William Kevin Kelly, Oliver Sartor, Joseph A. Smith

Bibliographic record

VenueCancer · 2004
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicinePosition statementProstate cancerRefractory (planetary science)OncologyCabozantinibProstateIntensive care medicinePsychological interventionInternal medicineRadiation therapyCancerGynecologyFamily medicine

Abstract

fetched live from OpenAlex

Because patients with hormone-refractory prostate carcinoma are a very diverse group, management of these patients represents a unique challenge. Despite much research, to the authors' knowledge few studies published to date have provided definitive treatment answers. The Society of Urologic Oncology (SUO) convened a multidisciplinary panel of urologists, oncologists, and radiation oncologists to develop a treatment algorithm for patients with hormone-refractory prostate carcinoma. The resulting treatment outline was based on a review of the literature review and on the expert opinions of the panelists. The current article provided a logical progression of treatment choices that included hormonal manipulations, chemotherapeutic options, and adjunctive therapies. Future clinical trials and therapies were also discussed by the authors. Management strategies should be targeted toward the individual patient. Although significant progress has been made in understanding and treating hormone-refractory prostate carcinoma, earlier interventions would be ideal and better therapeutic approaches to prolong survival are necessary.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.250

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 head0.041
GPT teacher head0.346
Teacher spread0.305 · 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

Citations36
Published2004
Admission routes1
Has abstractyes

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