Dekompresinė kraniektomija gydant aneurizminį subarachnoidinį kraujavimą
Bibliographic record
Abstract
Background: Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening neurological condition with high rates of mortality and disability. In cases where medical therapy fails to control elevated intracranial pressure (ICP), decompressive craniectomy (DC) may serve as a critical intervention. Aim: To assess the role of decompressive craniectomy in managing aSAH and its influence on clinical outcomes through a comprehensive literature review. Methods: A structured literature review was conducted using PubMed, yielding 502 articles. Thirty-nine studies were included based on predefined PICO-based eligibility criteria. Methodological quality was assessed using the Newcastle-Ottawa Scale for observational studies and the Joanna Briggs Institute checklist for systematic reviews. Results: DC was most frequently indicated in aSAH patients with refractory ICP, cerebral edema, midline shift, hematomas, herniation signs, and poor neurological grades. Early intervention, especially primary or ultra-early DC, was associated with reduced short-term mortality and improved intracranial dynamics. However, long-term functional outcomes remain poor in most cases, with only a minority achieving functional independence. The procedure carries significant complication risks, including infections, subdural hygromas, and psychological sequelae such as the syndrome of trephined. Surgical approaches vary, though larger bone flaps and wide duraplasty are associated with better outcomes. Conclusion: While decompressive craniectomy can be life-saving for select aSAH patients, its benefits are highly dependent on timing, patient selection, and surgical technique. Despite promising findings in specific contexts, a lack of standardized guidelines and high complication rates highlight the need for further multicenter prospective studies to refine indications and improve patient outcomes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".