The Hemoglobin A1C of African-Americans/Blacks with Diabetes Mellitus Type Two Using Low-Fat Diabetes Plate Diet
Bibliographic record
Abstract
Diabetes Mellitus type two (DMT2) is a chronic disease that leads to high blood sugar in the body and if not corrected over a period of time, it leads to development of complications. Some of those complications include blindness, kidney failure, and retinopathy, among others. DMT2 continues to affect many people in the USA, especially African-Americans/Black who have the highest prevalence as compared to other races. Proper diet management especially the Low-Fat diabetes diet helps to decrease Hemoglobin A1c, which could result in reduction of risk of developing complications and morbidity related to DMT2. This project was done to educate African-American/Black population in Benton Harbor Health Center with DMT2 on Low-fat diabetes diet. The results indicated a significantly lower hemoglobin A1c and BMI among patients that were in the experimental group who had education intervention as compared to those who were in the control group and did not receive any diet education. This project took place over three months for each participant and pre-test and posttest measures were reported for HbA1C, BMI, as well as fat-related diet knowledge.
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 | 0.000 | 0.001 |
| 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".