P.123 Metabolic acidosis and functional outcome after aneurysmal subarachnoid hemorrhage: an exploratory analysis
Bibliographic record
Abstract
Background: Little data exists on the impact of metabolic acidosis in aneurysmal subarachnoid hemorrhage (aSAH). Given its detrimental effects in critically ill patients, we inquired whether in patients with aSAH, metabolic acidosis (bicarbonate <22mmol/L) was associated with an increased risk of worse outcomes at 3 months (mRS >2). Methods: We performed a retrospective analysis of the CONSCIOUS-1 randomized control trial dataset including all patients who had at least three bicarbonate levels drawn. Bivariate and multivariate logistic regression models were used to assess for independent relationship between metabolic acidosis and functional outcome at 3 months. Delayed cerebral ischemia (DCI) was assessed for potential effect modification. Results: Three hundred and nineteen patients were included in our analysis. There was no difference in the proportion of poor outcome between those with or without metabolic acidosis on bivariate analysis (OR=1.022, p=0.949). However, amongst individuals who develop DCI, there was increased odds of unfavorable outcome when patients developed metabolic acidosis (OR=7.588, p=0.023). Conclusions: Individuals who develop delayed cerebral ischemia may benefit from having their bicarbonate level carefully monitored. More studies are needed to determine how the development of metabolic acidosis can be mitigated, and whether its prevention leads to improved 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".