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Prostate specific antigen dynamics and prostate cancer risk: A population-based study.

2022· article· en· W4212999409 on OpenAlexaffabout
Amanda Hird, Refik Saskin, Lisa Del Giudice, Girish S. Kulkarni, Robert K. Nam

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineProstate cancerProstate-specific antigenProstateCohortInternal medicineCancerRetrospective cohort studyPopulationUrologyLogistic regressionGynecologyOncology

Abstract

fetched live from OpenAlex

239 Background: Baseline prostate specific antigen (PSA) is a strong predictor of clinically significant prostate cancer. In a large cohort of patients undergoing opportunistic testing, our objective was to assess the association between first serum PSA in combination with other PSA predictors and risk of prostate cancer. Methods: This was a retrospective, population-based study in Ontario, Canada between 2010-2019. Men 40-75 years of age who underwent incident PSA testing with at least two PSA tests during the study period were included. Among men who underwent PSA testing, the PSA levels of patients who were diagnosed with prostate cancer were compared to patients who were not. Univariable and multivariable logistic regression analysis was used to compare any prostate cancer and clinically significant prostate cancer diagnosis (International Society of Urological Pathology [ISUP] grade group 2-5) between groups. Results: A total of 508,238 patients were included in our cohort (12,444 cases and 495,794 controls). The median follow-up time was 8.2 years (IQR: 7.0-9.1). Patients who were diagnosed with prostate cancer were older (median age 62 [IQR: 56-67] versus 56 years [IQR:50-63], standardized difference [SD]:0.61) and had a higher first PSA than patients who were not (median 4.79 [IQR:3.29-6.96] versus 0.96 ng/mL [IQR:0.58-1.69], SD:1.93). Fewer than 0.1% (248/261,463) of patients with a first PSA less than 1.0 ng/mL were diagnosed with prostate cancer during the study period. Our final multivariable model revealed that a first PSA above 2.0 ng/mL (adjusted odds ratio [OR] 6.64, 95%CI: 6.13-7.20, p < 0.001), a final PSA between 4.0 to 9.9 ng/mL and 10.0 to 20.0 ng/mL (adjusted OR 22.09, 95%CI: 20.58-23.71, p < 0.001 and adjusted OR 47.46, 95%CI: 43.28-52.05, p < 0.001, respectively) and change from first to final PSA per 365 days of 20.0 to 99.9% (adjusted OR 3.40, 95%CI: 3.21-3.60, p < 0.001) and more than 100% (adjusted OR 6.91, 95%CI: 6.29-7.59, p < 0.001) were strongly associated with the diagnosis of prostate cancer. The model performed well when predicting any prostate cancer, clinically significant prostate cancer, and when stratified by age. Conclusions: This study suggests that first PSA in combination with final PSA and rate of change can be used to predict prostate cancer diagnosis. Future studies will be conducted to determine the association with metastatic and lethal prostate cancer.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.848

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.101
GPT teacher head0.485
Teacher spread0.384 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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