Bibliographic record
Abstract
Introduction: Prostate cancer (PCa) awareness is generally limited, and screening rates are lower among racial and ethnic minority populations compared to the general population, contributing to delayed diagnosis and increased PCa-specific mortality.We investigated the utility of a novel decision aid (DA) to bolster patients' knowledge and improve their ability to participate in shared decision-making.Methods: We created a novel DA (Figure 1) directed at minority and other vulnerable men, and randomly surveyed patients attending a primary care clinic, with nearly a dozen physicians, in Newark, NJ, over eight months.Participants were randomly recruited to DA and non-DA cohorts at a rate of 2:1 irrespective of previous PSA screening.All patients completed a standardized pre-survey.The intervention cohort was then offered the DA.All participants then completed the post-survey within a week of their appointment.The post-survey for the DA cohort contained additional questions specific to the DA.Patient demographics were compared using Chi-squared analysis.DA utility was characterized through multivariate logistic regression.Results: One hundred patients (DA, n=69 and non-DA, n=31) were surveyed.There were no significant differences in age (p=0.618),race (p=0.380),educa-Poster 7: Oncology-Prostate (Part 2) MP 7.1.Table 1 (cont'd).Chi-squared analysis comparing patient demographics for DA and non-DA individuals Non-DA DA p English first language 0.616 No 30.00%23.50% Yes 70.00% 76.50% Education 0.314 Less than high school 13.30% 8.80% High school 70.00% 55.90% Undergraduate 10.00% 22.10% Graduate level education 6.70% 13.20% Home ownership 0.749 Own 10.00% 14.70% Rent 90.00% 85.30% Health insurance 0.934 Uninsured 10.00% 10.30% Medicaid 33.30% 35.30%
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.513 | 0.271 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".