Perioperative Variables Contributing to the Rupture of Intracranial Aneurysm: An Update
Bibliographic record
Abstract
BACKGROUND: Perioperative aneurysm rupture (PAR) is one of the most dreaded complications of intracranial aneurysms, and approximately 80% of nontraumatic SAHs are related to such PAR aneurysms. The literature is currently scant and even controversial regarding the issues of various contributory factors on different phases of perioperative period. Thus this paper highlights the current understanding of various risk factors, variables, and outcomes in relation to PAR and try to summarize the current knowledge. METHOD: We have performed a PubMed search (1 January 1991-31 December 2012) using search terms including "cerebral aneurysm," "intracranial aneurysm," and "intraoperative/perioperative rupture." RESULTS: Various risk factors are summarized in relation to different phases of perioperative period and their relationship with outcome is also highlighted. There exist many well-known preoperative variables which are responsible for the highest percentage of PAR. The role of other variables in the intraoperative/postoperative period is not well known; however, these factors may have important contributory roles in aneurysm rupture. Preoperative variables mainly include natural course (age, gender, and familial history) as well as the pathophysiological factors (size, type, location, comorbidities, and procedure). Previously ruptured aneurysm is associated with rupture in all the phases of perioperative period. On the other hand intraoperative/postoperative variables usually depend upon anesthesia and surgery related factors. Intraoperative rupture during predissection phase is associated with poor outcome while intraoperative rupture at any step during embolization procedure imposes poor outcome. CONCLUSION: We have tried to create such an initial categorization but know that we cannot scale according to its clinical importance. Thorough understanding of various risk factors and other variables associated with PAR will assist in better clinical management as well as patient care in this group and will give insight into the development and prevention of such a catastrophic complication in these patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".