Clinical evidence based review and systematic scientific review in the identification of malignant transformation of inverted papilloma
Bibliographic record
Abstract
BACKGROUND: Inverted papilloma (IP) is an unusual type of benign tumor that has high recurrence rates and the potential to transform into squamous cell carcinomas (SCC). The mechanism of the transformation process from IP to IP-SCC is uncertain and there is no consensus regarding the best practice for IP-SCC detection. The goal of this study is to identify the best clinical methods to detect for IP-SCC. METHODS: An evidence-based review was performed using Medline and Ovid to obtain all articles up to October 10th, 2019 pertaining to identification of IP malignant transformation. All manuscripts discussing clinical methods or biomarkers were included. RESULTS: Based on clinical research studies, convoluted cerebriform pattern and apparent diffusion coefficient values on Magnetic Resonance Imaging (MRI) can help differentiate benign IP from SCC and increased SUVmax on PET/CT is associated with higher probability of malignancy although not as specific. No consensus about the best biomarker for IP-SCC has been reached among researchers and continues to be exploratory. CONCLUSION: Endoscopy with biopsy is the gold standard practice to identify IP-SCC; however, MRI is the preferred imaging modality to recognize malignant transformation in cases where biopsy is difficult. Multiple biomarkers have shown positive results, but no single indicator with clinical significance for monitoring malignant transformation process has been found.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".