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Record W1689819823 · doi:10.5489/cuaj.920

Prostate gland biopsies and prostatectomies: an Ontario community hospital experience

2013· article· en· W1689819823 on OpenAlexaffvenueabout
Ken J. Newell, John F. Amrhein, Rashmikant J. Desai, Paul F. Middlebrook, Todd M. Webster, Barry W. Sawka, Brian F. Rudrick

Bibliographic record

VenueCanadian Urological Association Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsCampbell Scientific (Canada)South Bruce Grey Health Centre
Fundersnot available
KeywordsProstate glandMedicineProstateGeneral surgeryPathologyUrologyInternal medicineCancer

Abstract

fetched live from OpenAlex

OBJECTIVE: Transrectal ultrasound-guided core biopsies of the prostate gland and prostatectomies have become common procedures at many community hospitals in Canada, especially in the era of serum prostate-specific antigen (PSA) screening for prostate cancer. The Gleason grading of prostate cancer in biopsies and prostatectomies is a major determinant used for treatment planning. There is evidence in the literature that suggests important discordance between community hospital pathologists and urological pathologists with respect to the Gleason grading of prostate cancer. Our objective was to determine the diagnostic rates and Gleason scoring patterns for prostate gland biopsies and prostatectomies at our institution compared with the literature. METHODS: We conducted a retrospective review of all prostate gland biopsies and prostatectomies performed at the Grey Bruce Health Services from January 2005 to September 2005. We collected data from 194 biopsies and 44 prostatectomies. We obtained prebiopsy serum PSA levels and digital rectal exam results for all patients from urologists' office records. RESULTS: The average age for men having biopsies was 65.8 (standard deviation [SD] 8.6) years, and the average prebiopsy serum PSA level was 8.7 (median 7.1, SD 6.2) mug/L. The rates of diagnosis from prostate gland biopsies of benign (17.6%), high-grade prostatic intraepithelial neoplasia (11.0%), atypical small acinar proliferation suspicious for invasive malignancy (13.2%) and invasive prostatic adenocarcinoma (58.2%) at our institution were significantly different than those reported in the literature (p < 0.001). We observed a significant variation in the rates of these diagnoses among the community hospital pathologists in our study (p = 0.004). There was a strong correlation between the increasing number of positive core biopsy sites and increasing Gleason scores in biopsies (p < 0.001). There was also a strong correlation between increasing pre-biopsy serum PSA levels and increasing Gleason scores in biopsies (p < 0.001). A substantial proportion (21.9%) of the biopsies given the Gleason score of 6 had a Gleason score of 7 in the prostatectomy specimen. CONCLUSION: Our results showed a significant difference in prostate gland biopsy categorical diagnoses compared with the literature. There were also significant differences in categorical diagnoses of prostate gland biopsies among the community hospital pathologists in our study. The data identify a strong positive correlation between the increasing number of positive core biopsy sites and increasing Gleason scores in biopsies, as well as a strong positive correlation between increasing prebiopsy serum PSA levels and increasing Gleason scores in biopsies that revealed cancer. We would encourage other community hospital pathologists, in collaboration with their urologists, to review periodically their prostate gland pathology practices in an attempt to improve the uniformity of diagnoses.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.673
Threshold uncertainty score0.659

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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 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

Citations9
Published2013
Admission routes3
Has abstractyes

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