Complications of craniofacial resection for malignant tumors of the skull base: Report of an International Collaborative Study
Bibliographic record
Abstract
BACKGROUND: Advances in imaging, surgical technique, and perioperative care have made craniofacial resection (CFR) an effective and safe option for treating malignant tumors involving the skull base. The procedure does, however, have complications. Because of the relative rarity of these tumors, most existing data on postoperative complications come from individual reports of relatively small series of patients. This international collaborative report examines a large cohort of patients accumulated from multiple institutions with the aim of identifying patient-related and tumor-related predictors of postoperative morbidity and mortality and set a benchmark for future studies. METHODS: One thousand one hundred ninety-three patients from 17 institutions were analyzed for postoperative mortality and complications. Postoperative complications were classified into systemic, wound, central nervous system (CNS), and orbit. Statistical analyses were carried out in relation to patient characteristics, extent of disease, prior radiation treatment, and type of reconstruction to determine factors that predicted mortality or complications. RESULTS: Postoperative mortality occurred in 56 patients (4.7%). The presence of medical comorbidity was the only independent predictor of mortality. Postoperative complications occurred in 433 patients (36.3%). Wound complications occurred in 237 (19.8%), CNS-related complications in 193 (16.2%), orbital complications in 20 (1.7%), and systemic complications in 57 (4.8%) patients. Medical comorbidity, prior radiation therapy, and the extent of intracranial tumour involvement were independent predictors of postoperative complications. CONCLUSIONS: CFR is a safe surgical treatment for malignant tumors of the skull base, with an overall mortality of 4.7% and complication rate of 36.3%. The impact of medical comorbidity and intracranial tumor extent should be carefully considered when planning therapy for patients whose tumors are amenable to CFR.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".