Comparison of Functional Endoscopic Sinus Surgery (FESS) and Nasalisation for Nasal Polyposis: Meta-Analysis and Institutional Perspective from CHUK
Bibliographic record
Abstract
Background: Nasal polyposis causes significant morbidity worldwide, often requiring surgery when refractory to medical therapy.While Functional Endoscopic Sinus Surgery (FESS) is considered the global standard, the more radical nasalisation technique remains in use for severe or recurrent disease, particularly in resource-limited settings, such as the Centre Hospitalo-Universitaire de Kamenge (CHUK).Methods: This meta-analysis, aligned with the PRISMA guidelines, compared FESS and nasalisation in adults with nasal polyposis.Systematic searches (PubMed, EMBASE, Cochrane, Web of Science; Jan 1997-Oct 2025) identified comparative studies.Data extraction included design, interventions, outcomes (nasal obstruction, olfaction, facial pain, complications, recurrence, asthma/aspirin intolerance), and risk-of-bias (Newcastle-Ottawa).Pooled odds ratios (OR), standardized mean differences (SMD), absolute risk differences, and I 2 for heterogeneity were calculated using random-effects models (RevMan 5.3).Sensitivity, subgroup, and publication bias analyses (Egger's test) were conducted.Certainty was graded (GRADE).Results: Ten studies (n = 2301) met the inclusion criteria.FESS improved nasal obstruction and olfaction but had higher antrostomy stenosis and synechia.Nasalisation reduced recurrence and facial pain, particularly in asthma/aspirin-intolerant patients.Certainty: moderate (GRADE); risk-of-bias: moderate.Funnel plot revealed minimal publication bias.Conclusion: Both techniques are viable.In the Centre Hospitalo-Universitaire de Kamenge (CHUK)'s modernization pathway, FESS should be the default for most chronic rhinosinusitis with nasal polyps (CRSwNP), contingent on structured training and postoperative protocols; selective nasalisation remains appropriate for aggressive phenotypes and in comorbid asthma/aspirin-intolerant disease.This meta-analysis provides an evidence base for capacity building, knowledge How to cite this paper: Horugavye, L.,
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.050 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".