Role of neck dissections in the management of carotid body tumors
Bibliographic record
Abstract
Abstract Objective Carotid body tumors (CBTs) are rare neoplasms of the paraganglia at the carotid bifurcation. While typically benign, CBTs occasionally exhibit malignancy, metastasizing to nearby lymph nodes. Histopathologic analysis alone is insufficient to confirm malignancy, requiring metastases to non‐neuroendocrine tissue for a definitive diagnosis. The role of selective neck dissections (SNDs) in detecting malignancy and guiding subsequent management remains uncertain. Method A retrospective case series through electronic chart review was performed on 21 patients undergoing CBT surgeries between 2002 and 2022 at a Canadian institution. SNDs were performed on all 21 patients. Data collection included patient demographics, genetic and laboratory testing results, preoperative imaging, intraoperative and postoperative complications, histologic analysis of neck SND and tumor specimen, and follow‐up results. Results Of the 21 surgical resections, there were three cases (14.3%) of carotid artery injuries and six cases (28.6%) of nerve injuries. One patient (4.8%) experienced three intraoperative strokes. Three patients (14.3%) were found to have lymph node involvement, confirming malignancy, and underwent further treatment with radiotherapy. Interestingly, two patients with carotid injuries had malignant tumors, demonstrating a statistical significance between carotid injury and malignancy (OR 34.00, 95% CI: 1.48, 781.83, p = .041). Conclusion SNDs are a useful adjunct in detecting malignancy during CBT surgeries. The incidence of malignancy in CBT is low but not negligible, and SND should be considered in patients to prevent inadvertent underdetection of metastatic disease. This study's 14.3% incidence of malignancy suggests that there may be a rationale for considering the universal implementation of SND during CBT resections. Level of Evidence 4.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".