The relationship between p53 and the malignant transformation of nasal inverted papilloma: a meta-analysis
Bibliographic record
Abstract
Abstract Background The nasal inverted papilloma (NIP) is a neoplasm that originates from the mucosal lining of the nasal cavity and paranasal sinuses. A meta-analysis was conducted to investigate the link between p53 dysregulation and prognosis in patients with NIP. Methods Relevant original articles were identified through a comprehensive search in the PubMed, EMBASE, and Web of Science databases up until January 14, 2025. Results The studies showed low heterogeneity (I 2 = 31%), allowing the use of the fixed effect model (FEM). The forest plot revealed a significant association between p53 dysregulation and the malignant transformation and progression of NIP, with an odds ratio (OR) of 7.93 (95% CI 4.74–13.28, P < 0.001). Sensitivity analysis indicated a pooled OR ranging from 7.13 (95% CI 4.19–12.11, P < 0.001) to 11.39 (95% CI 6.00–21.60, P < 0.001). Significant correlations were also found in subgroups based on region, publication year, and Newcastle–Ottawa Scale (NOS) scores. Moreover, Begg's test (P = 0.26) and Egger's test (P = 0.57) results suggested a low risk of publication bias. Conclusions The meta-analysis underscores the strong relationship between p53 dysregulation and the malignant transformation of NIP. The practical applications of identifying p53 dysregulation in NIP patients have the potential to significantly impact clinical decision-making and patient outcomes.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.050 |
| Bibliometrics | 0.006 | 0.007 |
| 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".