Preoperative radiological features in predicting complications of carotid body tumor resection
Bibliographic record
Abstract
Objective Carotid body tumors (CBTs) are rare neoplasms that pose significant surgical challenges. This study aims to evaluate the predictive utility of preoperative radiological characteristics on postoperative complications in patients undergoing CBT resection at a tertiary care center. Methods A retrospective analysis was conducted on 106 patients who underwent CBT resection between 2003 and 2023. Patient demographics, tumor characteristics, and operative details were collected. The primary outcomes were an estimated blood loss (EBL) >250 mL and cranial nerve (CN) injury. Logistic regression models were used to identify significant preoperative radiological predictors, including Shamblin grade, Peking Union Medical College Hospital (PUMCH) grade, tumor distance to the base of the skull (DTBOS), and tumor volume. Results One hundred six patients were included. Higher Shamblin and PUMCH grades were significantly associated with increased EBL and CN injury. Specifically, the Shamblin grade alone predicted an EBL >250 mL with a McFadden R 2 value of 0.14, which slightly decreased to 0.13 when DTBOS and tumor volume were added. For CN injury, the Shamblin grade alone had an R 2 of 0.16, which significantly improved to 0.27 with the addition of DTBOS and further to 0.29 with tumor volume. The PUMCH grade alone predicted an EBL >250 mL with an R 2 value of 0.08, which did not significantly change with the addition of DTBOS and tumor volume. For CN injury, the PUMCH grade alone had an R 2 of 0.14, improving to 0.21 with DTBOS and to 0.22 with tumor volume. Furthermore, a 1-cm decrease in DTBOS significantly increased the odds of requiring a blood transfusion (odds ratio, 2.26; 95% confidence interval, 1.28-4.01; P = .0051) and the risk of CN injury (odds ratio, 3.65; 95% confidence interval, 1.98-6.73; P < .0001). Conclusions This study identified novel preoperative radiological predictors that enhance the predictive accuracy of standard classification systems, offering valuable insights for preoperative planning. Although the Shamblin and PUMCH classifications are useful tools on their own, our findings demonstrate that incorporating additional radiological features, such as DTBOS and tumor volume, can substantially increase their predictive utility. Surgeons are encouraged to incorporate multiple preoperative radiological variables alongside traditional classification systems to better assess the risk of postoperative complications. Further research with larger, multi-institutional cohorts are necessary to validate these findings and refine predictive models.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".