International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors
Bibliographic record
Abstract
BACKGROUND: Sinonasal neoplasms, whether benign and malignant, pose a significant challenge to clinicians and represent a model area for multidisciplinary collaboration in order to optimize patient care. The International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors (ICSNT) aims to summarize the best available evidence and presents 48 thematic and histopathology-based topics spanning the field. METHODS: In accordance with prior International Consensus Statement on Allergy and Rhinology documents, ICSNT assigned each topic as an Evidence-Based Review with Recommendations, Evidence-Based Review, and Literature Review based on the level of evidence. An international group of multidisciplinary author teams were assembled for the topic reviews using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses format, and completed sections underwent a thorough and iterative consensus-building process. The final document underwent rigorous synthesis and review prior to publication. RESULTS: The ICSNT document consists of four major sections: general principles, benign neoplasms and lesions, malignant neoplasms, and quality of life and surveillance. It covers 48 conceptual and/or histopathology-based topics relevant to sinonasal neoplasms and masses. Topics with a high level of evidence provided specific recommendations, while other areas summarized the current state of evidence. A final section highlights research opportunities and future directions, contributing to advancing knowledge and community intervention. CONCLUSION: As an embodiment of the multidisciplinary and collaborative model of care in sinonasal neoplasms and masses, ICSNT was designed as a comprehensive, international, and multidisciplinary collaborative endeavor. Its primary objective is to summarize the existing evidence in the field of sinonasal neoplasms and masses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.106 | 0.176 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.012 |
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".