Radiological score, asthma and NSAID‐exacerbated respiratory disease predict relapsing chronic rhinosinusitis
Bibliographic record
Abstract
OBJECTIVES: The aim was to evaluate the predictive potential of Sinonasal Radiological (SR) and the Lund-Mackay (LM) score of sinus computed tomography (CT) scans on postoperative relapses of chronic rhinosinusitis (CRS). MATERIALS AND METHODS: CRS patients (n = 483, 12-80 years) underwent routine sinus CT scans. The SR score was defined by obstructed frontal recess (0 = no, 1 = yes) and visualization of middle and inferior turbinate (0 = anatomy can be easily visualized, 1 = anatomy cannot be easily visualized) on each side (a total of 0-6 points). Associations were analyzed by nonparametric, survival and Cox's proportional hazard models. RESULTS: Revision endoscopic sinus surgery (ESS) was performed in 133 (28.0%) patients on average (min-max) of 3.2 (0-12) years after performing the sinus CT scans. Of the 408 patients who underwent the baseline ESS, high preoperative SR or LM scores significantly predicted revision ESS (p < 0.001) and peroral corticosteroid courses purchased during the follow-up (p = 0.009 and p < 0.001, respectively for SR- and LM-scores). In multivariable analysis, both SR score and asthma and/or NSAID exacerbated respiratory disease (N-ERD) were significantly associated with revision ESS risk (p = 0.035, p = 0.007, respectively). CONCLUSION: LM and SR and a history of asthma or N-ERD predict CRS relapses, which may help in decision-making.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".