Risk Factors for Malignant Transformation in Inverted Sinonasal Papilloma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: Inverted sinonasal papilloma (IP) is a benign epithelial proliferation that can recur and undergo malignant transformation. We performed a systematic review and meta-analysis to answer the following question: what are the risk factors for malignant transformation in IP? Methods: A search was performed in PubMed and Embase databases. Numbers of affected individuals in exposed versus non-exposed individuals, or odds ratio values, were compared for each specific risk factor examined. The Newcastle–Ottawa Scale (NOS) was used to assess the risk of bias. To assess the overall quality of evidence, we used the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Meta-analyses were conducted using the fixed-effects and the random effects models. Heterogeneity of the results was assessed by I2 statistic output. Meta-analyses and forest plots were obtained using Review Manager (RevMan) software version 5.4. Results: After examining 1875 results (942 from PubMed; 933 from Embase), 26 articles were selected. Among the 26 selected articles, the number of cases examined ranged from 14 to 162. All studies examined a population of 1271 IPs, with a carcinoma incidence of 230/1271 (18.1%). Three meta-analyses were performed for the following risk factors: smoking, alcohol, and HPV. Using the fixed-effects model, significant values were obtained for smoking (p = 0.002) and HPV (p < 0.001), with moderate and low quality of evidence, respectively. Alcohol did not reach statistical significance (p = 0.95). Conclusions: This study demonstrates that both smoking and HPV are risk factors for IP malignant transformation. Possible interventions include smoking cessation and HPV vaccination in individuals affected by IP.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".