Promotion of prostate cancer screening equity: A quality improvement education initiative.
Bibliographic record
Abstract
140 Background: Black men are disparately affected by prostate cancer (PC). They are more likely to develop PC and at an earlier age when the disease is more advanced at diagnosis. As a result, Black men are two to three times more likely to die from PC than white men. Given these disparities, experts increasingly promote screening for PC in Black men at a younger age. Methods: To inform implementation of a quality improvement education (QIE) project in three primary care practices in Maryland, a zip code analysis of the prevalence of PC was performed. Maryland practices were selected due to higher rates of PC in regions of the state and significant Black populations (Table). The QIE initiative started with a baseline practice assessment survey (including information on panel size, patient demographics, PC screening/treatment approaches, and barriers) and an analysis of current PC screening rates. Health system leaders and champions from the practice sites received training on patient-centered conversations with high-risk Black patients and concerning QIE planning. The champions developed rapid cycle improvement plans to implement increased screening using a patient-oriented online educational platform ( Dr. PSA ), as well as posters, and placards for patient education. Results: The overall national prevalence of PC in Medicare Fee-for-Service Program beneficiaries in 2018 was 2.65%. For Black beneficiaries the overall prevalence was 2.89%. Prevalence for beneficiaries in specific Maryland zip codes are detailed in Table. *Data not available; Source: NMQF Prostate Cancer Index Baseline practice assessment data revealed that patient panels ranged from 4,000 to 58,163 patients, with Black patients accounting for 50% or more of two of the practices and 25 to 50% of the third practice. Barriers to screening identified include financial issues, insurance restrictions, and lack of knowledge about PC and screening. Baseline screening rates are approximately 75%. Conclusions: Zip code prevalence analysis and baseline practice assessment data confirmed the relevance of implementing a QIE initiative in the three selected sites. Through a mixed-methods evaluation study, practice staff knowledge, attitudes, and self-reported practices will be assessed pre- and post-QIE initiative to assess impact of the initiative and determine opportunities for further improvement in PC screening practices.[Table: see text]
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".