Evaluation of inverted papilloma recurrence rates and factors associated recurrence after endoscopic surgical resection: A retrospective review
Bibliographic record
Abstract
BACKGROUND: Sinonasal inverted papillomas (IP) are benign tumours arising from the mucosal lining of the nasal cavity and paranasal sinuses with a high propensity for recurrence and malignant transformation. Advances in endoscopic surgery and improved radiologic navigation have increased the role of endoscopic surgical resection in the treatment of IPs. The current study aims to evaluate the rate of IP recurrence after endoscopic endonasal resection and to evaluate factors which impact recurrence. METHODS: This was a single-centre retrospective chart review of all patients who underwent endoscopic sinus surgery for management of IP between January 2009 and February 2022. Primary outcomes were the rate of IP recurrence and time to IP recurrence. Secondary outcome measures were patient and tumour factors that contributed to IP recurrence. RESULTS: Eighty-five patients were included. The mean age was 55.7 and 36.5% of patients were female. The mean follow-up time was 39.5 months. Of the 85 cases, 13 cases (15.3%) had recurrence of their IP and the median time to recurrence was 22.0 months. All recurrent tumours recurred at the attachment site of the primary tumour. The univariate analysis did not identify any significant demographic, clinical, or surgical predictors of IP recurrence. There were no significant changes in sinonasal symptoms at the time IP recurrence was detected. CONCLUSION: Endoscopic endonasal resection of IPs represents an effective surgical approach, however, the relatively high rate of recurrence and lack of symptomatic changes at the time of recurrence necessitates long term follow up. Better delineation of risk factors for recurrence can help identify high-risk patients and inform postoperative follow up strategies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".