Outcomes of sinonasal inverted papilloma resection by surgical approach: an updated systematic review and meta‐analysis
Bibliographic record
Abstract
Background Selecting the optimal surgical approach for resection of sinonasal inverted papilloma (SIP) remains a challenge, with endoscopic, external, and combined approaches being utilized. This systematic review was conducted as an update to a 2006 systematic review to determine the preferred surgical approach for tumor control. Methods The study protocol was developed a priori following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) process. Data were collected and outcomes were analyzed according to surgical approach and sites of tumor involvement. Results A total of 96 papers and 4134 SIP patients were included. The overall recurrence rate was 12.80% (322/2515) for the endoscopic approach group, 16.58% (182/1098) for the external approach group, and 12.60% (65/516) for the combined approach group. Meta‐analysis by random effects model showed that the summarized risk ratio (RR) of recurrence was 0.61 (95% confidence interval [CI], 0.44 to 0.85, p = 0.003), I 2 = 37.95% for the endoscopic vs external approach; 0.98 (95% CI, 0.69 to 1.39, p = 0.901), I 2 = 9.06% for the endoscopic vs combined approach; 1.61 (95% CI, 1.06 to 2.43, p = 0.025), I 2 = 0.00% for the external vs combined approach. After adjusting for publication bias, the adjusted RRs were 0.66 ( p = 0.014) for endoscopic vs external; 0.99 ( p = 0.955) for endoscopic vs combined; and 1.33 ( p = 0.224) for external vs combined. Conclusion An enlarging and maturing body of literature continues to indicate that endoscopic approaches result in significantly lower recurrence rates than open approaches for surgical resection of SIP.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.002 | 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".