Allergic fungal rhinosinusitis - a new staging system
Bibliographic record
Abstract
BACKGROUND: The existing Kupferberg post-operative endoscopic staging system for allergic fungal rhinosinusitis (AFRS) has 4 stages (0-3) based on the `global` appearance of one side of the nose. Patients may however show visual improvement and yet remain at the same stage due to persistence in one sinus cavity, thus making the staging system ineffective. The aim of this study was to validate a new system that allows greater sensitivity in characterising the inflammation seen endoscopically. METHODOLOGY: A series of endoscopy videos of 50 patients with AFRS were retrospectively staged using a new ten-grade system, scoring each sinus cavity (maxillary, ethmoid, frontal and sphenoid) from 0-9 for increasing mucosal oedema and 1 point for the presence of fungal mucin giving a maximum score of 40 for each side of the nose. To assess reliability, 4 independent rhinologists were also asked to score the videos using the new system. RESULTS: A greater variety in the spectrum of mucosal disease was demonstrated with the new system allowing for a more descriptive analysis of its severity and its response, or lack of, to treatment. The inter-class correlation between the 6 total observers was 0.86 (95% CI: 0.83, 0.92). CONCLUSION: Use of the new staging system provides a more sensitive tool for following patients` progress post-operatively in allergic fungal rhinosinusitis and in determining their response to treatment.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".