En bloc Resection of Solitary Cranial Tumors: An Algorithmic Reconstructive Approach
Bibliographic record
Abstract
Background This study analyzes the surgical outcomes for single setting surgeries involving en-bloc solitary calvarial tumor resection in combination with three-layered reconstruction, presenting a novel planning algorithm. Methods Data were retrieved for all patients undergoing single-stage tumor excision, using our novel three-layered reconstructive approach (duraplasty, cranioplasty, and soft tissue reconstruction) between 2005 and 2017 at a single tertiary hospital center. Patients ≥18 years with a Karnofsky Performance score (KPS) >70 and a life expectancy of > 2 months were included. Patient characteristics, surgical specifics, histological diagnoses, outcomes, and complications were reviewed. Results Eighteen single-staged excisions and three-layered reconstructions were performed. Seven patients presented with primary tumors and 11 patients with metastases. Mean age was 62 years. Mean follow-up time was 39 months. Primary closure was used in 12 of 18 patients, microvascular free flap with skin grafting in 4 of 18, and local advancement or rotational flap in 2 of 18. Two compromised free flaps were revised. There was no flap necrosis, skin graft failure, or wound infection observed in this series. Neurosurgical complications included two cases with seizures, one sublesional intraparenchymal hematoma, one adjacent parenchymal infarct, one case of delayed postradiation cerebrospinal fluid leakage, and one case of subdural hemorrhage. Conclusion En-bloc excision followed by three-layered reconstruction is a feasible and often suitable single-stage technique for complex solitary metastasis or primary calvarial tumors, which historically have been challenging to treat. It can offer an alternative approach to primary and metastatic calvarial tumors other than palliative treatment or hospice care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".