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Record W2734423176 · doi:10.5489/cuaj.4324

Case: Spontaneous regression of post-radical prostatectomy prostate-specific antigen elevation without adjuvant therapy in a patient with lymph node metastasis

2017· article· en· W2734423176 on OpenAlexaffvenue
Taehyoung Lee, Yanbo Guo, Saahil Vij, Rahul Bansal, Nathan C. Wong, Bobby Shayegan

Bibliographic record

VenueCanadian Urological Association Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsProstatectomyMedicineProstate cancerProstate-specific antigenStage (stratigraphy)PathologicalOncologyLymph nodeSurgical marginUrologyAdjuvant therapyMetastasisAdjuvantBiopsyInternal medicineCancer

Abstract

fetched live from OpenAlex

Prostate cancer remains the most frequently diagnosed cancer among men. The combination of clinical stage, serum prostate-specific antigen (PSA), and Gleason score (biopsy) assists in predictive assessment of pathological stage and prognosis. Furthermore, pathological criteria, including Gleason score, surgical margin status, extracapsular extension, seminal vesicle invasion, and lymph node involvement, provide prognostication in patients undergoing radical prostatectomy (RP). In this paper, we present a case of a patient with high-risk prostate cancer with persistent PSA elevation post-RP who experiences a complete regression of PSA without any adjuvant therapy. To the authors' knowledge, such a finding has not been described in the literature previously.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0100.006
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 head0.019
GPT teacher head0.253
Teacher spread0.234 · 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 designCase report
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

Citations5
Published2017
Admission routes2
Has abstractyes

Explore more

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