Perceived family history risk and symptomatic diagnosis of prostate cancer
Bibliographic record
Abstract
BACKGROUND: Prostate cancer (PrCA) is the most common cancer and the second leading cause of cancer death among US men. African American (AA) men remain at significantly greater risk of PrCA diagnosis and mortality than other men. Many factors contribute to the experienced disparities. METHODS: Guided by the Health Belief Model, the authors surveyed a population of AA and Caucasian men newly diagnosed with PrCA to describe racial differences in perceived risk of PrCA and to examine whether 1) perceived high risk predicts greater personal responsibility for prostate care; and 2) greater personal responsibility for prostate care predicts earlier, presymptomatic diagnosis. Multivariate general linear modeling was performed. RESULTS: The authors found that men with a PrCA family history appreciated their increased risk, but AA men with a family history were less likely to appreciate their increased risk. Nevertheless, neither reporting a PrCA family history nor perceived increased risk significantly predicted screening and preventive behaviors. Furthermore, higher physician trust predicted increased likelihood to have regular prostate exams and screening, indicating that the racial differences in seeking prostate care may be mediated through physician trust. Expressed personal responsibility for screening and more frequent preventive behaviors were associated with more frequent screening diagnoses, fewer symptomatic diagnoses, and less frequent advanced cancers. CONCLUSIONS: Together, these results indicate that appreciating greater risk for PrCA is not sufficient to ensure that men will intend, or be able, to act. Increased trust in physicians may be a useful, central marker that efforts to reduce disparities in access to medical care are succeeding.
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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 | 0.004 |
| 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) | 0.003 | 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".