Smoking and Drinking Adjusted Association between Head and Neck Cancers and Oral Health Status Related to Periodontitis: a Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Not so many reports about the association between head and neck cancer (HNC) and oral health status related to periodontitis (OHS-P) has been published in different countries with different methods. So, there is a need for an extensive meta-analysis with the total articles published until 2020. Hence, this study aimed to estimate the association between HNC and OHS-P through a meta-analysis. METHODS: Based on Preferred Reporting Items for Systematic Reviews and Meta Analyses guidelines, 22 studies were selected through PubMed and Cochrane Library databases. Meta-analysis using them was performed to evaluate the association. The risk of bias assessment using the Newcastle-Ottawa Scale (NOS) was applied to evaluate the quality of non-randomized studies. Publication bias was evaluated by funnel plot and Egger's regression test. RESULTS: = 0.66). Moreover, the association was higher in 10 fair or good NOS studies (OR, 3.08) and in 7 Asian studies (OR, 2.68), which were from the fixed model without publication bias. CONCLUSION: Our meta-analysis showed that bad OHS-P was associated with the risk of HNC. The association was stronger in studies using ABL or CAL for assessing periodontitis.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.053 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".