Association between oral lichen planus and Candida albicans infection: a systematic review and meta-analysis
Bibliographic record
Abstract
This study examines the potential association between Oral Lichen Planus (OLP) and Candida albicans infection, exploring its potential impact on the development of OLP. A meta-analysis of individual case-control studies was performed, estimating odds ratios (ORs) and their corresponding 95% confidence intervals (CIs). A quality assessment of the literature was conducted using the Newcastle-Ottawa Scale (NOS). Due to considerable heterogeneity in the selected studies, subgroup analyses were performed based on geographical location and recruitment methods. No significant publication bias was detected. A sensitivity analysis validated the robustness of the findings when applying a random-effects model. The meta-analysis included ten studies, comprising 1,124 OLP patients and 1,063 healthy controls. Results indicated a significantly higher detection rate of Candida albicans in OLP patients compared to healthy controls (OR = 1.74, P = 0.003, 95% CI: 1.20, 2.52). Additionally, an increased risk of Candida albicans infection was observed in erosive OLP (E-OLP) patients compared to healthy controls (OR = 3.97, 95% CI: 2.31, 6.84, P < 0.00001). These findings suggest a complex interplay between OLP and Candida albicans, highlighting the need for further research to elucidate the varying susceptibilities among different clinical types of OLP. This study provides novel insights for future research directions and clinical treatment strategies in this field.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| 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".