Assessment of periodontal conditions and systemic disease in older subjects
Bibliographic record
Abstract
BACKGROUND: Osteoporosis (OPOR) is a common chronic disease, especially in older women. Patients are often unaware of the condition until they experience bone fractures. Studies have suggested that OPOR and periodontitis are associated diseases and exaggerated by cytokine activity. Panoramic radiography (PMX) allows studies of mandibular cortical index (MCI), which is potentially diagnostic for OPOR. AIMS: i). To study the prevalence of self-reported history of OPOR in an older, ethnically diverse population, ii). to assess the agreement between PMX/MCI findings and self-reported OPOR, and iii). to assess the likelihood of having both a self-reported history of OPOR and a diagnosis of periodontitis. MATERIALS AND METHODS: PMX and medical history were obtained from 1084 subjects aged 60-75 (mean age 67.6, SD +/- 4.7). Of the films, 90.3% were useful for analysis. PMXs were studied using MCI. The PMXs were used to grade subjects as not having periodontitis or with one of three grades of periodontitis severity. RESULTS: A positive MCI was found in 38.9% of the subjects, in contrast to 8.2% self-reported OPOR. The intraclass correlation between MCI and self-reported OPOR was 0.20 (P < 0.01). The likelihood of an association between OPOR and MCI was 2.6 (95%CI: 1.6, 4.1, P < 0.001). Subjects with self-reported OPOR and a positive MCI had worse periodontal conditions (P < 0.01). The Mantel-Haentzel odds ratio for OPOR and periodontitis was 1.8 (95%CI: 1.2, 2.5, P < 0.001). CONCLUSIONS: The prevalence of positive MCI was high and consistent with epidemiological studies, but only partly consistent with a self-reported history of osteoporosis with a higher prevalence of positive MCI in Chinese women. Horizontal alveolar bone loss is associated with both positive self-reported OPOR and MCI.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".