P.095 Role of selective neck dissections in the management of carotid body tumours
Bibliographic record
Abstract
Background: Carotid body tumours (CBT) are rare neoplasms of the paraganglia at the carotid bifurcation. Histopathologic analysis alone is insufficient to confirm malignancy, requiring metastases to non-neuroendocrine tissue including cervical lymph nodes for definitive diagnosis. The role of selective neck dissection (SND) during CBT surgeries in detecting malignancy and guiding subsequent management remains uncertain. Methods: A retrospective case series was performed on all patients undergoing CBT surgeries with SND between 2002 and 2022. Data collection included demographics, genetic and laboratory testing, imaging, intra- and post-operative complications, follow-up and histopathology. Results: Twenty-one patients underwent CBT resection with SND. Of these, 3 had carotid artery injuries, and 5 had nerve injuries. One patient experienced peri-operative embolic strokes, presumed related to tumour embolization. Three patients were found to have lymph node involvement, confirming malignancy. Malignancy was significantly associated with the risk of carotid injury (p = 0.04.) Conclusions: SND is a useful adjunct in detecting malignancy during CBT resection. The incidence of malignancy in CBT is low but not negligible and SND should be considered in patients with suspected malignancy or high-risk factors. This study’s 14% incidence of malignancy suggests there may be a rationale for considering universal implementation of SND during CBT resection.
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 | 0.001 | 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) | 0.002 | 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".