Evaluating modifiable hypertension risk in Nigerian adults—The Nigerian diet risk score
Bibliographic record
Abstract
AIMS: Our study aimed to derive and validate a diet risk score for clinical use in Nigeria to screen for hypertension risk and evaluate its association against a panel of cardiovascular biomarkers. METHODS: The Nigerian dietary screening tool was used to collect dietary intake data from 151 participants visiting the River State University Teaching Hospital, Port Harcourt, Nigeria, for routine medical care. Blood samples were collected from a subsample (n = 94) for biomarker assessment. Multiple logistic regression was used to derive the Nigerian diet risk score for hypertension. Internal validation of the Nigerian diet risk score for hypertension was performed using measures of discrimination and calibration. Mediation analysis was used to evaluate the biomarker-mediated effects of the diet risk score for hypertension on hypertension. All statistical analyses were performed in R. RESULTS: Each one-point increment in Nigerian diet risk score (on a scale of 0 to 30) was associated with a twofold increase in odds of hypertension (odds ratio: 2.04, 95% confidence interval [CI]: 1.16, 3.58, p = 0.01), with the highest score associated with >18-fold increased odds of hypertension, compared to lowest Nigerian diet risk score for hypertension. The score demonstrated good discrimination (area under the curve: 0.92, 95% CI: 0.80, 1.00) with a high sensitivity (0.85) and specificity (0.94). Additionally, mediation analysis suggested that the association between Nigerian diet risk score for hypertension and blood pressure is partly explained by shared biological pathways that mediate cholesterol, triglycerides, LDL-C, CRP and homocysteine levels. CONCLUSION: The resulting Nigerian diet risk score for hypertension is a valuable tool for clinicians to identify individuals at risk of hypertension, and will advance community efforts in the prevention and management of hypertension in Nigeria.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".