Expression of Ki-67 in Oral Lichen Planus: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
Statement of the Problem: One of the main signs of cancer development is increasing of cell proliferation activity. Expression of the Ki-67 as a cell proliferation marker is extensively utilized in pathology studies as an indicator of proliferation in human tumors. According to studies, Ki-67 plays an effective role in the pathology of malignant and pre-malignant oral mucosa lesions. Purpose: The current study aimed to systematically review the Ki-67 expression in oral lichen planus without dysplasia and compare it with oral epithelial dysplasia. Materials and Method: In this meta-analysis, all articles in the English language were searched in databases including Web of Science, PubMed, Embase, Scopus, and Google Scholar until July 2023. MeSH terms and free keywords were used in the search step. Expression of Ki-67 in oral lichen planus and oral epithelial dysplasia was analyzed by Comprehensive Meta-Analysis software. Results: Nine hundred and two articles related to the searched words were found. According to the selection criteria, 12 retrospective articles were selected. Low quality was not observed in any of the records by the Newcastle-Ottawa scale and most of them had a relatively good quality. Totally, 593 patients were examined. The heterogeneity between studies was not significant. The meta-analysis results indicated a significantly lower Ki-67 expression in oral lichen planus without dysplasia in comparison to oral epithelial dysplasia. Conclusion: The more intense expression level of Ki-67 in oral epithelial dysplasia compared with oral lichen planus was observed. The ki-67 expression could be utilized to indicate the existence and intensity of epithelial dysplasia and disease progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".