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Record W2012974026 · doi:10.5489/cuaj.510

Comparison of 8, 10, 12, 16, 20 cores prostate biopsies in determination of prostate cancer and importance of prostate volume

2014· article· en· W2012974026 on OpenAlexvenueno aff
Cavit Ceylan, Ömer Gökhan Doluoğlu, Erdoğan Ağlamış, Özkan Baytok

Bibliographic record

VenueCanadian Urological Association Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsProstate cancerMedicineProstateRectal examinationCancer detectionCancerBiopsyUrologyProstate biopsyCore (optical fiber)Core biopsyRadiologyGynecologyInternal medicineBreast cancer

Abstract

fetched live from OpenAlex

INTRODUCTION: In this study, we evaluate the relationship between increasing core numbers and cancer detection rate. METHODS: We included 1120 patients with prostate-specific antigen levels ≤20 ng/mL and/or suspicious digital rectal examination findings in this study. All patients had a first-time prostate biopsy and 8, 10, 12, 16, and 20 core biopsies were taken and examined in different groups during the study. Multiple logistic regression analysis was made to reach the factor affecting the cancer detection rate between the patients with and without cancer. A p < 0.05 was considered statistically significant. RESULTS: Out of 1120 patients, 221 (19.7%) had prostate cancer. Again of the total 1120 patients, 8 core biopsies were taken from 229 (20.4%); 10 core biopsies from 473 (42.2%); 12 core biopsies from 100 (8.9%); 16 core biopsies from 140 (12.5%); and 20 core biopsies from 178 (15.9%) patients. The increase in the core number increased the cancer detection rate by 1.06 times (p = 0.008). CONCLUSIONS: As long as prostate volume increases, increasing the core number elevates the cancer detection rate. Thus, the rate of missed cancer will be reduced and the rates of unnecessary repetitive biopsy decreases.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.278
Teacher spread0.261 · 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

Citations19
Published2014
Admission routes1
Has abstractyes

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