Adapting Diabetes Risk Scores for Jordan
Bibliographic record
Abstract
BACKGROUND: Diabetes risk score can be used as a simple non-invasive screening tool for identifying people with high risk of diabetes. This study aimed to assess the predictive power of various risk-scoring systems to predict pre-diabetes and diabetes in Jordanian adults. METHODS: This cross-sectional study was conducted among people attending 54 primary health care centers distributed throughout the 12 governorates of Jordan. Diabetes risk scores using the American Diabetes Association risk score, Canadian risk score, Finland risk score (FINDRISC), British Risk score, German and Australian risk score were calculated for each patient. Fasting blood sugar (FBS) was measured for all participants. RESULTS: This study included 392 participants: 231 patients with normal fasting blood sugar (FBG), 101 patients with pre-diabetes and 60 patients with type 2 diabetes. The FINDRISC, British, and Australian risk scores were strongly inter-correlated and weakly correlated with other systems' risk scores. Moreover, they correlated moderately and significantly with FBS. In contrast, other systems risk scores were associated weekly with FBS. Based on receiving operating characteristics (ROC) analysis and multivariate logistic regression, the FINDRISC risk score was superior to other risk scores to predict high FBS and identify pre-diabetes and diabetes. CONCLUSION: FINDRISC risk score performed the best compared to other risk scores for predicting pre-diabetes, diabetes, and absence of diabetes. We recommend using the FINDRISC risk score assessment in Jordan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".