Effectiveness of the Endoscopic Prelacrimal Recess Approach for Maxillary Sinus Inverted Papilloma Removal: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BackgroundConventional minimally invasive surgery has a high recurrence rate, and nasal morbidity can occur if the scope of surgery is expanded to complete removal of maxillary sinus inverted papilloma.ObjectiveTo analyze the efficacy of the endoscopic prelacrimal recess approach (EPLRA) for maxillary sinus inverted papilloma removal.MethodsEighteen studies were included in this meta-analysis. Articles comparing the prelacrimal recess approach with conventional surgery (endoscopic surgery or the Caldwell–Luc operation) for inverted papilloma removal were included. Outcomes of interest included recurrence and postoperative morbidities. The methodological quality was assessed using the Newcastle–Ottawa scale.ResultsThe recurrence rates of inverted papilloma, postoperative facial or gingival numbness, and alar collapse were 3.13% (95% confidence interval [CI]: 1.32, 7.27), 9.02% (95% CI: 3.70, 20.39), and 3.39% (95% CI: 1.28, 8.68), respectively. The recurrence rate of inverted papilloma was significantly lower after the EPLRA than after conventional surgery (odds ratio [OR] = 0.2290; 95% CI: 0.0808, 0.6489). However, there were no significant differences between the procedures in the rates of facial or gingival numbness (OR = 0.4567; 95% CI: 0.1497, 1.3933), epistaxis (OR = 0.3150; 95% CI: 0.0471, 2.1044), or periorbital swelling (OR = 1.2405; 95% CI: 0.1205, 12.7731).ConclusionsThe EPLRA can preserve the lacrimal system and is useful for maxillary sinus inverted papilloma removal due to a lower recurrence rate compared with conventional surgeries.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".