Surgical outcomes for carotid body tumour resection without preoperative embolization: a 10-year experience
Bibliographic record
Abstract
Background: Carotid body tumours (CBTs) are neoplasms originating from the paraganglionic cells of the carotid body. Excision is the main route of treatment. This study sought to assess the surgical outcomes of post-carotid body tumour resection without preoperative embolization and discern any underlying relationships between modified Shamblin classes (MSC) and related complications. Methods: A retrospective medical record review of prospectively collected data is performed at Sulaymaniyah Teaching Hospital between 2008 and 2019, for 54 patients. Presurgical and postsurgical variables such as comorbidities and complications were noted, respectively. Results: Patient ages ranged between 26 and 60 years (x̄=40.06) with a minimal female predominance (57.4%). Complications included one minor stroke. MSC and postoperative complications were significantly related (P≤0.001). Our analyses also suggested a significant relationship between intraoperative blood loss and the incidence of postoperative complications (P=0.001, χ²=25). The MSC III subtype was significantly associated with intraoperative blood loss (P=0.000), length of stay (P=0.000), and operating time (P=0.001). Conclusions: Our study purports a strong relationship between greater MSC and complications of all types. As such, surgeons may benefit from preoperative strategies to minimize complications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".