Pre-operative co-morbidities are predictors of death in young but not in elderly patients with intracranial aneurysmal rupture
Bibliographic record
Abstract
Background and Goals: There are no previous studies that showed the effects of aging on mortality predictors in patients who had aneurysmal sub arachnoid hemorrhage (SAH) (1). In the present study, we determined preoperative mortality predictors in younger and elderly individuals who had a repair procedure for a ruptured intracranial aneurysm. Materials and Methods: After ethical approval, we reviewed the charts of 434 patients who suffered aneurysmal SAH. In-hospital and out-of-hospital events for one month after discharge were recorded. Univariate analysis including Chisquared and Fisher exact tests were used to evaluate the hypothesis that death is associated with well-defined pre-, intra- and post-operative factors. Results and Discussions: The mortality rate was 14.87 & 18.78% in patients = 65 & >65 years old respectively. All reviewed patients had aneurysmal repair procedures within 15 days of SAH. There was no significant difference in the WFNS and GCS scores on admission between the younger and elderly groups. Table below shows the p-values of the main pre-operative mortality predictors.Table: No Caption Available.WFNS: World Federation Neurosurgical Societies, GCS: Glasgow Coma Scale. p < 0.05 is statistically significant. Conclusion(s): Interestingly, the results indicate that only in younger patients pre-operative co-existing diseases can predict mortality post aneurysmal SAH requiring a repair procedure. However, initial neurological and radiological findings can predict mortality in both age groups. Therefore, better control of pre-operative co-morbidities might decrease the incidence of death after repair procedures for ruptured intracranial aneurysms in younger compared to elderly patients.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".