Relationship between salivary haemoglobin and number of remaining teeth in older Koreans
Bibliographic record
Abstract
OBJECTIVE: We investigated the relationship between saliva and dental biofilm characteristics, oral hygiene behaviours and the number of remaining teeth in a sample of older Koreans. MATERIALS AND METHODS: This cross-sectional study included 133 participants, aged 65 years and older, recruited from senior welfare facilities in Incheon, South Korea. The outcome variable was the number of remaining teeth, and its associations with the independent variables of participants' general characteristics, salivary haemoglobin level, dental biofilm acidogenicity, salivary flow rate and oral hygiene behaviours were assessed. For statistical analyses, chi-squared test and step-wise multiple linear regression were used. RESULTS: The multiple linear regression model, which included all related factors identified in the bivariate analyses, showed that older adults who had high salivary haemoglobin levels (P < .05), brushed their teeth using the horizontal scrub method (P< .001), and did not use an interproximal cleaning device (P < .01) were more likely to have fewer remaining teeth. However, dental biofilm acidogenicity was not associated with the number of remaining teeth. CONCLUSION: The number of remaining teeth was associated with salivary haemoglobin level, appropriate toothbrushing technique and interdental cleaning. These findings suggest that the monitoring of salivary haemoglobin may contribute to the prevention of tooth loss caused by periodontal disease.
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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 | 0.000 | 0.001 |
| 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".