When Everything Revolves Around Internal Carotid Artery: Analysis of Different Management Strategies in Patients With Very Advanced Cancer Involving the Skull Base
Bibliographic record
Abstract
Internal or common carotid artery encasement (CAE) is observed in almost 2-7% of head and neck cancers (HNC) and designates the tumor with the T4b category. This clinical scenario is associated with a dismal prognosis, owing to the risk for thrombosis and bleeding that usually characterizes such an advanced cancer. Standardized radiological criteria to infer invasion of the carotid artery are lacking. Complete surgical resection in the context of a multimodality treatment is supposed to offer the greatest chances of cure. Surgery can either be carotid-sparing or include carotidectomy. Data on probability of cerebrovascular and non-cerebrovascular complications, risk of carotid blowout, poor oncologic outcomes, and less-than-certain efficacy of diagnostic and interventional preventive procedures against cerebral infarction make it difficult to define surgery as the recommended option among other therapeutic strategies. Non-surgical therapies based on radiation therapy possibly combined with chemotherapy are more frequently employed in HNC with CAE. In this context, carotid blowout is the most feared complication, and its probability increases with tumor stage and cumulative radiation dose received by the vessel. The use of highly conformal radiotherapies such as intensity-modulated particle therapy might substantially improve the manageability of HNC with CAE by possibly reducing the risk of late sequalae. Despite evidence is frail, it appears logical that a case-by-case evaluation through multidisciplinary decision making between head and neck surgeons, radiation oncologists, medical oncologists, diagnostic and interventional radiologists, and vascular surgeons are of paramount value to offer the best therapeutic solution to patients affected by HNC with CAE.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".