<scp>Computed Tomography</scp> Imaging Patterns of Sinonasal Inverted Papillomas: Comparison of Primary and Recurrent Disease
Bibliographic record
Abstract
OBJECTIVE: To analyze clinical and radiographic features that may impact the rate of focal hyperostosis (FH) on computed tomography (CT) for primary and recurrent sinonasal inverted papillomas (IPs) as well as highlight factors that may affect concordance between FH and IP true attachment point (TAP). METHODS: All IPs resected between 2006 and 2022 were retrospectively reviewed. CTs were read by a neuroradiologist blinded to operative details. IP with malignancy was excluded. Operative reports and long-term follow-up data were evaluated. RESULTS: Of 92 IPs, 60.1% had FH, 25% had no CT bony changes, and 20.7% were revision cases. The recurrence rate for rhinologists was 10.5% overall and 7.3% for primary IPs. Primary and revision IPs had a similar rate of FH (63% vs. 52.6%; p = 0.646) and FH-TAP agreement (71.7% vs. 90%; p = 0.664). Nasal cavity IPs, especially with septal attachment, were more likely to lack bony changes on CT (57.1%) compared to other subsites (p = 0.018). Recurrent tumors were 16 mm larger on average (55 mm vs. 39 mm; p = 0.008). FH (75.0% vs. 60.9%; p = 0.295), FH-TAP concordance (91.7% vs. 74.4%; p = 0.094), and secondary IP (18.8% vs. 20.3%; p = 0.889) rates were similar between recurrent and nonrecurrent tumors. CONCLUSION: Primary and revision IPs have a similar rate of FH and FH-TAP agreement. Nasal cavity IPs are less likely to exhibit bony CT changes. Lower recurrence was associated with smaller size and fellowship training but not multiple TAPs, revision, FH absence, or FH-TAP discordance. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:1591-1596, 2024.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".