Development and Validation of a Clinical Prediction Model to Diagnose Sinonasal Inverted Papilloma Based on Computed Tomography Features and Clinical Characteristics
Bibliographic record
Abstract
Objectives: Sinonasal inverted papilloma (SNIP) is one of the most common benign tumors of the nasal cavity and sinuses and is at risk for recurrence and malignant transformation. It is crucial to precisely predict SNIP before surgery to determine the optimal surgical technique and prevent SNIP recurrence. This study aimed to evaluate the diagnostic value of computed tomography (CT) features and SNIP clinical characteristics and to develop and validate a clinically effective nomogram. Methods: Here, 267 patients with SNIP and 273 with unilateral chronic rhinosinusitis with/without nasal polyps were included. Patient’s demographic and clinical characteristics (i.e., gender, age, nasal symptoms, history of sinus surgery, smoking, and alcohol dependence) and CT features (i.e., lobulated/wavy edge, air sign, focal hyperostosis, diffuse hyperostosis, focal osseous erosion, and CT values) were recorded. Independent risk factors were screened using logistic regression analysis. A nomogram model was developed and validated. Results: Logistic regression analysis showed that age, facial pain/headache, history of sinus surgery, lobulated/wavy edge, air sign, focal hyperostosis, focal osseous erosion, and CT values were independent predictors of SNIP. A nomogram comprising these 8 independent risk factors was established. The area under the curve (AUC) for the training set was .960 (95% CI, .942–.978) and the AUC for the validation set was .951 (95% CI, .929–.971). Conclusion: The obtained results suggested that the nomogram based on age, facial pain/headache symptoms, history of sinus surgery, and CT characteristics had an excellent diagnostic value for SNIP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".