Outcomes of a Modified Treatment Ladder Algorithm Using Retrobulbar Amphotericin B for Invasive Fungal Rhino-Orbital Sinusitis
Bibliographic record
Abstract
PURPOSE: To assess whether a modified treatment ladder algorithm incorporating transcutaneous retrobulbar amphotericin B (TRAMB) for invasive fungal rhino-orbital sinusitis can reduce the risk of exenteration without compromising survival. DESIGN: Retrospective, comparative clinical study with historical control subjects. METHODS: Fifty consecutive patients with biopsy-proven invasive fungal sinusitis and radiographic evidence of orbital involvement were evaluated at a single tertiary institution from 1999-2020. TRAMB was incorporated as part of the treatment algorithm in 2015. Demographics, underlying immune derangement, infective organism, ophthalmic examination, surgical care, and survival were compared in a quasiexperimental pre-post format, dividing patients into a pre-2015 group and a post-2015 group. Risk of exenteration and mortality were the primary outcomes. RESULTS: Baseline characteristics did not differ significantly between the 2 groups. Nearly all patients underwent a surgical intervention, most commonly functional endoscopic sinus surgery with debridement. TRAMB was administered to 72.7% of the post-2015 group. Exenteration was more common in the pre-2015 group (36.4% vs 9.1% [95% confidence interval {CI} 5.2-48.8]; P = .014), while mortality was similar (40.0% vs 36.7% [95% CI -22.1 to 29.3]; P = .816). After adjusting for potential confounders, patients treated after 2015 were found to have lower risk of exenteration (relative risk 0.28 [95% CI 0.08-0.99]; P = .049) and similar risk of mortality (relative risk 1.04 [95% CI 0.50-2.16]; P = .919). CONCLUSION: Compared with historical control subjects, patients with invasive fungal rhino-orbital sinusitis who were treated with a modified treatment ladder algorithm incorporating TRAMB had a lower risk of disfiguring exenteration without an apparent increase in the risk of mortality.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".