Paranasal sinus occupancy assessed from magnetic resonance images—associations with clinical indicators in patients with systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVES: Nasal, paranasal sinus and mucosal disorders are common symptoms in autoimmune rheumatic diseases. Soft tissue changes and fluid accumulation in the osteomeatal complexes and paranasal sinuses manifest as opaqueness on radiological images which can be assessed using visual scoring and computational methods on CT scans, but their results do not always correlate. Using MRI, we investigate the applicability of different image analysis methods in SLE. METHODS: We assessed paranasal sinus opaqueness on MRI from 51 SLE patients, using three visual scoring systems and expert-delineated computational volumes, and examined their association with markers of disease activity, inflammation, endothelial dysfunction and common small vessel disease (SVD) indicators, adjusting for age and sex-at-birth. RESULTS: The average paranasal sinus volume occupation was 4.55 (6.47%) [median (interquartile range) = 0.67 (0.25-2.65) ml], mainly in the maxillary and ethmoid sinuses. It was highly correlated with Lund-Mackay (LM) scores modified at 50% opaqueness cut-off (Spearman's ρ: 0.71 maxillary and 0.618 ethmoids, P < 0.001 in all), and with more granular variations of the LM system. The modified LM scores were associated with SVD scores (0: B = 5.078, s.e. = 1.69, P = 0.0026; 2: B = -0.066, s.e. = 0.023, P = 0.0045) and disease activity (anti-dsDNA: B = 4.59, s.e. = 2.22, P = 0.045; SLEDAI 3-7: 2.86 < B < 4.30; 1.38 < s.e. < 1.63; 0.0083 ≤ P ≤ 0.0375). Computationally derived percent opaqueness yielded similar results. CONCLUSION: In patients with SLE, MRI computational assessment of sinuses opaqueness and LM scores modified at a 50% cut-off may be useful tools in understanding the relationships among paranasal sinus occupancy, disease activity and SVD markers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".