Prelacrimal window approach to the maxillary sinus: a systematic review and meta-analysis of the literature
Bibliographic record
Abstract
BACKGROUND: The prelacrimal window approach (PLWA) is a minimally invasive surgical technique that has been proposed as an alternative to the traditional approaches to access the maxillary sinus. METHODOLOGY: A systematic review with meta-analysis was performed following PRISMA guidelines and identified 368 articles for initial review of which 14 (610 participants) met the criteria for meta-analysis. Four databases, including PubMed, Google Scholar, Web of Science and Scopus, were searched to identify relevant articles. Two independent reviewers conducted the eligibility assessment for the included studies. Methodology quality and risk of bias were evaluated by New Castle Ottawa scale. The outcomes assessed were recurrence of the pathology, postoperative morbidity including epiphora, dry nose, facial, gingival numbness, epistaxis or local infection. RESULTS: The present data suggest a significant reduction in the recurrence rate of maxillary sinus pathology following PLWA when compared to conventional surgery (endoscopic medial maxillectomy, endoscopic sinus surgery and the Caldwell-Luc operation). The rates of epiphora, facial or gingival numbness, epistaxis or infection requiring intervention, were not significantly different between the procedures. CONCLUSIONS: Maxillary sinus pathology can be effectively treated using the PLWA technique, as it has been shown to result in a lower recurrence rate compared to 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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.019 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".