External Validation of a Periodontal Prediction Model for Identification of Diabetes among Saudi Adults
Bibliographic record
Abstract
Aim and objective: To externally validate the performance of a novel periodontal prediction model (PPM) for identification of diabetes among Saudi adults. Materials and methods:The study was carried out among 150 adults attending primary care clinics in Riyadh (Saudi Arabia).The study adopted a temporal external validation approach, where the performance of the PPM was evaluated in the same location as the development study, but at a later time to allow for some variation between samples.A case-control approach was adopted, where diabetes status was first ascertained, followed by the completion of the Finnish Diabetes Risk Score (FINDRISC), Canadian Diabetes Risk (CANRISK) tools, and periodontal examinations.Results: The area under the curve (AUC) of the PPM (based on the number of missing teeth, the proportion of sites with pocket probing depth ≥ 6 mm, and mean pocket probing depth) was 0.514 (95% CI: 0.385, 0.642).The FINDRISC and CANRISK tools had AUC values of 0.871 (95% CI: 0.811-0.931)and 0.927 (95% CI: 0.884-0.971),respectively.The addition of the PPM did not improve the AUC of FINDRISC (p = 0.479) or CANRISK (p = 0.920).The decision curve analysis showed that there was no clinical benefit in adding the PPM to either tool.The PPM was updated with an overall adjustment factor for all existing predictors and three more periodontal measures.Conclusion: In an external sample, the PPM had poor performance for identification of diabetes and no added value when combined with FINDRISC and CANRISK.The performance of the PPM improved after recalibration and extension. Clinical significance:The results underscore the value of externally validating prediction models before applying them in clinical dental practice.
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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".