Recurrences following treatment of proliferative verrucous leukoplakia: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: A systematic review and meta-analysis were made of the incidence of recurrences in patients with proliferative verrucous leukoplakia (PVL) subjected to different types of treatment. METHODS: The study was carried out following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines. A literature search was made in the Medline (PubMed), EMBASE, and Web of Science databases, together with a manual search, covering the period from 1985 to January 2020, with no language restrictions. Studies were included if they described treatments applied to at least 10 patients with the corresponding outcomes. Methodological quality was evaluated using Jadad scale and Newcastle-Ottawa tool. Global incidence was calculated by random effects meta-analysis using the Comprehensive Meta-analysis version 3.0 software. Publication bias was assessed using funnel plots and the Duval and Tweedie trim and fill method. RESULTS: Of the 922 identified articles, 12 were found to meet the inclusion criteria. Most of them presented moderate or low risk of bias. A total of 397 patients were analyzed. The mean age was 62.34 years and 248 were women (62.5%). The mean follow-up was 79.3 months. The most frequent treatment was surgical removal with a cold scalpel or laser (339 patients). A total of 232 subjects presented lesion recurrence. The combination of proportions global effect meta-analysis yielded a recurrence rate of 67.2% (95% CI: 48.3-81.8), with the absence of publication bias. CONCLUSIONS: There is not enough scientific evidence to conclude that any treatment strategy is able to reduce the recurrence in PVL.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.050 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".