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Record W2062724290 · doi:10.1002/cncr.23085

Neoadjuvant therapy followed by prostatectomy for clinically localized prostate cancer

2007· review· en· W2062724290 on OpenAlexaff
Guru Sonpavde, Kim N., Thomas Powles, Christopher J. Sweeney, Noah M. Hahn, Thomas E. Hutson, Matthew D. Galsky, William R. Berry, Dov Kadmon

Bibliographic record

VenueCancer · 2007
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineProstatectomyProstate cancerUrologyProstateNeoadjuvant therapyCancerOncologyInternal medicineBreast cancer

Abstract

fetched live from OpenAlex

The results of this assessment of the literature indicated that neoadjuvant therapy followed by prostatectomy may improve long-term outcomes for patients with high-risk localized disease. In addition, this approach provides a paradigm for evaluating the activity and mechanism of action of new agents as correlative studies are facilitated by the availability of tumor tissue before and after therapy. The authors determined that a multidisciplinary approach involving oncologists, urologists, and pathologists is critical to the success of this model. Recent and ongoing studies of neoadjuvant therapy followed by prostatectomy were reviewed.

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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.102
GPT teacher head0.460
Teacher spread0.358 · 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
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

Citations39
Published2007
Admission routes1
Has abstractyes

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