Anatomic Diagram as a Novel Assessment Strategy for Subclinical Local Residual Disease in Sinonasal Squamous Cell Carcinoma and Intestinal‐Type Adenocarcinoma
Bibliographic record
Abstract
OBJECTIVE: In the last two decades, transnasal endoscopic surgery (TES) has become pivotal in the management of sinonasal tumors. This approach involves a multiblock tumor resection, adding complexity to the interpretation of surgical margins after pathological examination. This study compares different strategies to infer subclinical local residual disease (SLRD), aiming to identify and validate the best available method for assessing SLRD after transnasal endoscopic resection of sinonasal squamous cell carcinoma (SCC) and intestinal-type adenocarcinoma (ITAC). METHODS: Three methods to estimate SLRD (as either absent-R0 - or microscopically present-R1) were applied in patients who received negative margins-aimed endoscopic resection: sole-pathologist examination, multidisciplinary evaluation, and anatomic diagram-based assessment. The primary outcome to compare methods was time-to-recurrence (TTR) stratification provided by these methods. RESULTS: 105 patients were included (50 SCC and 55 ITAC). All three methods resulted significantly associated with TTR in both ITAC and SCC populations. In a multivariate model, only SLRD assessed with the anatomical diagram was independently associated with time-to-local-recurrence (TTLR) in SCC and TTR in both ITAC and SCC groups. The concordance index (C-index), the area under the curve (AUC), and the incremental AUC (iAUC) were higher for the anatomical diagram method in the ITAC and SCC cohorts. CONCLUSION: The anatomic diagram proved to be the best available strategy, yet with limitations, for assessing SLRD, demonstrating superior TTR stratification compared to traditional methods.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".