Accuracy and precision of using partial-mouth recordings to study the prevalence, extent and risk associations of untreated periodontitis
Bibliographic record
Abstract
To study the accuracy and precision of estimating the prevalence, extent and associated risks of untreated periodontitis using partial-mouth recording protocols (PRPs) Methods: A purposive sample of 431 individuals who had never been treated for periodontal disease was recruited from screening clinics at the King Saud bin Abdul-Aziz University for Health Sciences. Data were collected using questionnaires and clinical examinations. The prevalence, extent and risk associations of periodontitis were evaluated. Three PRPs were compared to full-mouth recordings (FRPs) in terms of the sensitivity, specificity, predictive values, and absolute bias. Results: The prevalence of periodontitis was estimated with the highest accuracy and precision by examinations of the full mouth at the mesiobuccal and distolingual sites (FM)MB-DL, followed by random half-mouth (RHM) recordings. The extent of periodontitis was estimated with high precision using all the PRPs, and the absolute bias ranged from −0.6 to −2.3. The absolute bias indicated by OR for risk associations was small for the three PRPs and ranged from −0.8 to 0.8. Conclusion: (FM)MB-DL and RHM were the PRPs with moderate to high levels of accuracy and precision for estimating the prevalence and risk associations of periodontitis. The extent of periodontitis was estimated with high precision using all three PRPs. The results of this study showed that the magnitude and direction of bias were associated with the severity of periodontitis, the selected PRPs and the magnitude of the risk associations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".