Primary Care Cancer and Diabetes Complications Screening of Black Women with Type 2 Diabetes
Bibliographic record
Abstract
PURPOSE: To determine the frequency with which Black women with type 2 diabetes receive routine primary health care screening for cancer and diabetes complications. DATA SOURCES: Pilot study data from a convenience sample of 21 Black women (mean age 46.8 years) with type 2 diabetes. CONCLUSIONS: Cancer screening consisted of Pap smear, mammography, and colon cancer screening consistent with current American Cancer Society recommendations. Ninety percent reported having had a Pap smear, 86% mammogram and 33% colon cancer screening. Diabetes complications screening was based on the American Diabetes Association care recommendations. Fifty-five percent received screening eye exams, 40% were screened for renal proteinuria, and 50% received foot examinations and diabetes foot care instruction. IMPLICATIONS FOR PRACTICE: This sample of mid-life, Black, educated, working women with type 2 diabetes utilize healthcare services and have high rates of primary care cancer screening. Rates of diabetes complications screening are less than optimal. Because Black American women suffer disproportionately high rates of diabetes and related complications, it is imperative that they receive quality diabetes care in an effort to improve health outcomes and decrease premature mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".