P2-225 Prostate-specific antigen testing awareness and participation in New South Wales, Australia: demographic, lifestyle and health-related factors
Bibliographic record
Abstract
Background Although the prostate-specific antigen (PSA) test is widely used to screen for prostate cancer, there is very little information on the characteristics of men who are aware of the PSA test, and their patterns of PSA testing. Methods A cross-sectional study used computer assisted telephone interviews to collect data in New South Wales, Australia. Multinomial logistic regression identified the factors independently associated with the awareness of, and participation in PSA testing. Results Of the 6100 men, 39% were unaware of the PSA test, 12% were aware of the PSA test but never tested, 14% had a non-recent PSA test, and 35% had a recent PSA test. Unaware men were more likely to be born outside Australia (OR=1.19; 95% CI 0.88 to 1.60), have a blue-collar occupation (OR=1.38; 95% CI 1.00 to 1.91), be a current smoker (OR=1.99; 95% CI 1.30 to 3.05), or have benign prostatic hyperplasia (BPH) (OR=1.70; 95% CI 1.07 to 2.71), and less likely to have completed a higher school certificate (OR=0.44; 95% CI 0.24 to 0.79), or live in inner regional areas (OR=0.59; 95% CI 0.44 to 0.80). Men who did not have a recent test, were more likely to visit the doctor (OR=1.38; 95% CI 1.05 to 1.82), or have BPH (OR=2.70; 95% CI 1.74 to 4.20), and were less unsure of their risk of developing prostate cancer (OR=0.61; 95% CI 0.37 to 1.00). Men who had a recent test were more likely to visit the doctor (OR=2.57; 95% CI 1.99 to 3.33), have BPH (OR=3.87; 95% CI 2.58 to 5.81), or have a higher perceived risk of developing prostate cancer (OR=1.99; 95% CI 1.22 to 3.26), and less likely to be other than married (OR=0.65; 95% CI 0.47 to 0.91). Conclusions As men's PSA testing experience varied by demographic, lifestyle and health-related factors, it is important for policymakers and physicians to consider these when communicating about PSA testing.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".