Predictors of prostate‐specific antigen testing in men aged ≥55 years: A cross‐sectional study based on patient‐reported outcomes
Bibliographic record
Abstract
OBJECTIVES: To examine the predictors of prostate-specific antigen discussion with a physician and prostate-specific antigen testing in men aged ≥55 years. METHODS: Utilizing the USA Health Information National Trends Survey, 4th Ed., a cross-sectional study from 2011 to 2014 was carried out to analyze the factors predicting prostate-specific antigen testing and discussion in men ≥55 years. Associations between each covariate and prostate-specific antigen discussion/testing were determined. Multivariable logistic regression models were used to determine clinically relevant predictors of prostate-specific antigen discussion/testing. Due to multiple comparisons, the Bonferroni correction was used. RESULTS: A total of 2731 men included in the Health Information National Trends Survey were analyzed. Several socioeconomic parameters were found to increase the likelihood of men aged ≥55 years to undergo prostate-specific antigen testing: living with a spouse, a higher level of education (college graduate or above), a higher income (>$50 000 annually) and previous history of any cancer. In contrast, current smokers were less likely to undergo prostate-specific antigen testing. Having a prostate-specific antigen discussion with a physician was more likely for men surveyed in 2014, for men who were living with a spouse, who had a higher annual income (>$50 000 annually) and those with a history of any cancer. CONCLUSIONS: Significant inequalities in prostate-specific antigen testing and discussion exist among men in the USA, mainly driven by socioeconomic factors. Ideally, prostate-specific antigen testing and discussion should be based on relevant clinical factors with a shared decision-making approach for every man. Therefore, a better understanding of the socioeconomic factors influencing prostate-specific antigen testing/discussions can inform strategies to reduce existing gaps in care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".