Oral health in individuals with bleeding disorders: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Individuals with bleeding disorders have been reported to have a number of oral health issues due to varying conditions. A comprehensive evaluation of the different oral health conditions has not been carried out in the past. This systematic review and meta-analysis was carried out to collate and critically analyse existing research, and provide a comprehensive overview of the current state of knowledge on oral health. METHODS: A comprehensive search was conducted in electronic databases, including PubMed, Scopus and Embase, in October 2023. No restriction on time frame or language was applied. The risk of bias for cross-sectional studies was assessed using the Agency for Healthcare Research and Quality (AHRQ) tool, and case control studies were assessed using the New Castle Ottawa Scale (NOS). RESULTS: Twenty-two articles were included in the final analysis with a total sample size of 2422 subjects. Of the 22 articles assessed, nine quantitative assessments were included in the Meta analysis. Pooled data analysis was carried out. A total of 13 studies reported medium risk whereas the remaining nine studies showed low risk of bias. The weighted mean DMFT scores in individuals with bleeding disorders were found to be 2.43 [0.62. 4.24], mean dmft was 2.79 [1.05, 4.53] and mean OHI-S was reported to be 1.79 [1.00, 2.57], respectively. CONCLUSION: The findings emphasize that these individuals have fair oral hygiene and lower dmft/DMFT scores. Oral bleeding emerged as an important oral health component to be cautiously dealt with particularly during the stages of exfoliation/shedding.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".