Predictors of Postoperative Bleeding After Cranial Surgery: The Role of Perioperative and Tumor-Related Factors
Bibliographic record
Abstract
Postoperative hemorrhage (POH) is a rare but serious complication of cranial neurosurgery, often resulting in neurological deterioration and necessitating urgent surgical intervention. Despite its clinical relevance, POH remains underreported and insufficiently understood. This study aimed to identify potential risk factors including perioperative variables and tumor-related characteristics associated with POH requiring surgical evacuation. A total of 1862 cranial tumor procedures were performed in our department over a 10-year period. Data on perioperative parameters and tumor characteristics were retrospectively collected and analyzed. Statistical analyses were conducted to assess associations of them to POH. Statistical analysis revealed several peri- and postoperative variables significantly associated with POH in univariate analyses. These included intraoperative blood loss (p = 0.012) and length of postoperative hospital stay (p = 0.016). Furthermore, the outcomes measured using the Glasgow Outcome Scale (p < 0.001) and the Karnofsky Performance Scale (p < 0.001) showed also statistical relevance as a result of postoperative bleeding in these patients. The findings suggest that specific perioperative factors particularly intraoperative blood loss are associated with an increased risk of POH after intracranial tumor surgery. Additionally, prolonged hospitalization and worsened functional outcomes were linked to the occurrence of postoperative hemorrhage. In contrast, tumor-specific characteristics and routine laboratory values showed no significant association with hemorrhagic complications in this cohort.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".