P.131 Outcome prediction in patients with aneurysmal subarachnoid hemorrhage undergoing microsurgical aneurysm repair: analysis of a South Australian Cerebrovascular Registry
Bibliographic record
Abstract
Background: Accurate outcome prediction among patients with aneurysmal subarachnoid haemorrhage (aSAH) has remained elusive. We aimed to identify outcome predictors and develop a model to guide clinicians and the families of patients who are being considered for microsurgical repair of a ruptured aneurysm. Methods: We identified 246 consecutive patients with aSAH who underwent microsurgical clipping of the culprit aneurysm between 01/09/2011 and 20/07/2020. Independent predictors of outcome were identified using logistic regression and an outcome prediction model was developed. Results: Age>55 (OR3.35, 95%CI 1.06–10.56, p=0.04), high-grade aSAH (WFNS≥4) (OR7.82, 95%CI 2.66–22.98, p<0.001) and midline shift of ≥5mm (OR10.35, 95%CI 3.22–22.23, p<0.001) were all associated with unfavourable outcome (mRS≥4) at a mean of 87.27 (±53.40) days after ictus. Age>55 was also associated with inpatient mortality (OR4.98, 95%CI 1.83–13.54, p=0.002) and unfavourable outcome at final follow-up (OR3.76, 95%CI 1.26–11.20, p=0.002). Furthermore, midline shift of >5mm was significantly associated with inpatient mortality (OR5.55, 95%CI 1.74–17.64, p=0.004) and unfavourable outcome at final follow-up (OR9.71, 95%CI 3.25–29.04, p<0.001). Conclusions: Older age, poorer presenting WFNS grade and increased mass effect are all associated with poorer outcome among patients undergoing microsurgical clipping of a ruptured aneurysm. These data have been used to construct an outcome prediction model for these 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".