The added value of periodontal measurements for identification of diabetes among Saudi adults
Bibliographic record
Abstract
BACKGROUND: The aims of this study were to develop a prediction model for identification of individuals with diabetes based on clinical and perceived periodontal measurements; and to evaluate its added value when combined with standard diabetes screening tools. METHODS: The study was carried out among 250 adults attending primary care clinics in Riyadh (Saudi Arabia). The study adopted a case-control approach, where diabetes status was first ascertained, and the Finnish Diabetes Risk Score (FINDRISC), Canadian Diabetes Risk questionnaire (CANRISK), and periodontal examinations were carried out afterward. RESULTS: A periodontal prediction model (PPM) including three periodontal indicators (missing teeth, percentage of sites with pocket probing depth ≥6 mm, and mean pocket probing depth) had an area under the curve (AUC) of 0.694 (95% Confidence Interval: 0.612-0.776) and classified correctly 62.4% of participants. The FINDRISC and CANRISK tools had AUCs of 0.766 (95% CI: 0.690-0.843) and 0.821 (95% CI: 0.763-0.879), respectively. The addition of the PPM significantly improved the AUC of FINDRISC (P = 0.048) but not of CANRISK (P = 0.144), with 26.8% and 9.8% of participants correctly reclassified, respectively. Finally, decision curve analysis showed that adding the PPM to both tools would result in net benefits among patients with probability scores lower than 70%. CONCLUSIONS: This study showed that periodontal measurements could play a role in identifying individuals with diabetes, and that addition of clinical periodontal measurements improved the performance of FINDRISC and CANRISK.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".