Sinonasal inverted papilloma recurrence rates and evaluation of current staging systems
Bibliographic record
Abstract
BACKGROUND: Sinonasal inverted papillomas (SNIPs) are benign epithelial growths with high recurrence rates after surgical management. This study aims to evaluate SNIP recurrence rates after endoscopic surgery and to provide a comparison of published staging systems. METHODS: This chart review evaluated primary and revision SNIP cases from January 2008 to December 2016 at a tertiary sinus centre. Data was collected on patient demographics, origin site, surgical approaches, follow-up duration, recurrence, and smoking history. Each case was staged using Krouse, Oikawa, Cannady, Han, and Kamel systems. RESULTS: 52 primary and 22 revision SNIP patients had a mean follow-up of 42.3 (range:3-55) months. 11 primary cases (21.1%) and 5 revision cases (22.7%) had recurrences. Primary and revision cases had a mean time to recurrence of 24.0 (range:3-55) and 14.6 (range:10-20) months respectively. Smoking history had an OR of 0.63 (CI 95%: 0.18-2.22) for recurrence. The age group of 20-39 years featured the highest rates of recurrence. Patient groups defined by each staging system were compared by Kaplan-Meier survival analyses and logrank tests. Chi-squared values for Krouse, Oikawa, Cannady, Han, and Kamel systems were 6.73, 7.02, 6.19, 8.23 and 3.29, respectively. CONCLUSION: Recurrence rates found in this study are comparable to published literature. No statistical significance was found to associate smoking with recurrence. Han and Cannady staging systems were found to define patient groups that correlated well with recurrence. Staging systems should play a role in the management of SNIPs, especially to identify patients requiring additional post-surgical monitoring.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".