When is a multidisciplinary surgical approach required in sinonasal tumours with cranial involvement?
Bibliographic record
Abstract
The term “sinonasal tumours” includes a large spectrum of diseases, which are characterized by heterogeneous biological behavior and prognosis, and located in a critical anatomic area. Diagnosis and treatment of sinonasal tumours require the contribution of different disciplines. A narrative review was performed to highlight the role of surgeons in contributing to a multidisciplinary approach to sinonasal tumours. Diagnosis and staging of sinonasal tumours is challenging and requires collaboration between surgeons, radiologists, and pathologists. The identification and management of critical extensions (orbital or intracranial encroachment, vascular abutment or encasement) is fundamental for successful treatment. Most cases of advanced sinonasal tumours can undergo surgical intervention by an adequately trained otorhinolaryngological team. The contribution of neurosurgeons and oculoplastic surgeons is required in selected scenarios. In rare circumstances, multidisciplinary reconstructive strategies can be indicated for complex tissue defects. Furthermore, a multidisciplinary approach is pivotal in the management of perioperative complications. While surgery remains the mainstay of treatment, the role of non-surgical adjuvant or even exclusive treatments is constantly expanding.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".