Computed tomography angiography lightbulb sign: Characteristic enhancement pattern on neck computed tomography angiography in differentiating paraganglioma from schwannoma of the carotid space
Bibliographic record
Abstract
It is important to correctly distinguish paragangliomas from other tumors such as schwannomas in the preoperative assessment of head and neck tumors because paragangliomas have a propensity to bleed profusely during surgery. Therefore, preoperative embolization is often required while with schwannomas preoperative embolization is generally not required. Occasionally, schwannomas can mimic paragangliomas on routine computed tomography and magnetic resonance imaging of the neck. In this study, we retrospectively evaluated the computed tomography angiography of the neck of 10 patients with carotid space tumors. Seven patients had pathologically proven paraganglioma while three patients had schwannomas. We describe the "computed tomography angiography lightbulb sign" as avid homogeneous enhancement in the arterial phase which can accurately distinguish these entities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".