PD50-02 PROSTATE SPECIFIC ANTIGEN DYNAMICS AND PROSTATE CANCER RISK: A POPULATION-BASED STUDY
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: While there is disagreement between professional medical organizations about which patients should undergo Prostate Specific Antigen (PSA) screening, there are no guidelines that recommend PSA screening more often than annually.Despite this, there are patients who have their PSA tested more frequently, which we have termed "Prosteria".We aimed to characterize patient demographic and temporal trends of frequent PSA testing.METHODS: Adult men with at least three PSA tests and two or more years of continuous insurance enrollment in the Optum Deidentified ClinformaticsÒ Data Mart were included in this study.Men were censored at the end of their insurance enrollment or 30 days prior to a diagnosis of prostate cancer, prostatitis, or UTI.PSA tests were excluded if they occurred within 30 days of a prior PSA test."High frequency PSA testing" was defined as an average of one or more PSA tests every 9 months.Multivariable logistic regression evaluated associations between demographic features and PSA testing frequency.RESULTS: 9,547,876 PSA tests obtained between January 2003 and June 2019 on 1,996,472 men were included.While 55% of men were tested less than annually, 19% of men underwent high frequency testing.The distribution of time between PSA tests was notable for spikes around 90, 180, and 365 days.Patients were less likely to undergo high frequency testing if they were White (OR: 0.73, 95% CI: [0.72, 0.73]), educated (OR: 0.91, 95% CI: [0.91, 0.92]), and younger (OR: 0.98 per year, 95% CI: [0.97-0.98]).However, a U-shaped distribution was observed with men aged 55-65 being less likely to undergo high frequency testing than younger or older men.Of men with PSA levels available, men with high frequency testing had higher average PSA values (1.99 (SD: 1.94) vs 1.31 (SD: 1.30); p < 0.001).Over the study period, the proportion of patients getting high frequency testing increased at a rate of 1.17% per year (figure).CONCLUSIONS: In this large cohort study, high-frequency PSA testing was common, increasing over time, and significantly associated with demographic characteristics.Future work will investigate variations between ordering providers based on geographic region and specialty, as well as the impact of screening frequency on rates of prostate biopsy, cancer detection and treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".