Systematic Review of Granulomatous Invasive Fungal Sinusitis Management
Bibliographic record
Abstract
ABSTRACT Objectives Granulomatous invasive fungal sinusitis (GIFS) affects immunocompetent individuals. There is ongoing debate over whether surgery, antifungal medication, or a combined approach is the best treatment. This article summarizes reports about GIFS and its management. Methods Eight search engines, gray literature, and review articles were searched. Two independent reviewer groups screened the eligibility of articles. An independent reviewer solved disagreements. Exclusion criteria included non‐English language reports, papers with unavailable full‐texts, reviews, publications before 1980, and studies lacking information about GIFS management. Results Of the 279 identified articles, 41 studies were included (n = 89 patients). Sinonasal GIFS with skull‐base/intracranial extension was associated with an increase in mortality (p = 0.002, OR = 14.083; 95% CI = 1.753–113.157). Treatment was associated with an 87.2% remission rate (p < 0.001, OR = 7.818; 95% CI = 4.502–13.576); a combined medical and surgical approach had a 74.2% recovery rate. Of surgical interventions, the highest recovery rates were associated with endoscopic debulking (52.5%), extensive surgical debulking (32.5%), and open sinonasal approach (15%, p = 0.132). The utilization of voriconazole was associated with higher recovery rates, but this was not significant (76.9 vs. 56%, p = 0.548). Conclusion Sinonasal GIFS with skull‐base/intracranial extension is associated with higher mortality rates. The superiority of the endoscopic debulking and voriconazole protocol in managing these cases warrants further investigation. Level of Evidence Level 4.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".