P.093 Hemoglobin values, fluctuations from baseline, and transfusion as predictors of outcome following aneurysmal subarachnoid hemorrhage
Bibliographic record
Abstract
Background: Anemia following aneurysmal subarachnoid hemorrhage (aSAH) has been associated with poor outcome, but complications from transfusion have limited aggressive management of anemic patients. This study examined the relationship between hemoglobin levels, transfusion and outcome following aSAH. Methods: We performed a post-hoc analysis of the CONSCIOUS-1 trial. Poor outcome was defined as a 3-month modified Rankin Scale > 2. Minimum hemoglobin levels were evaluated as predictors of outcome using logistic regression analysis, ROC curve analysis, and LOWESS curves. Propensity score matching was used to assess the effect of transfusion on poor outcome in patients with minimum hemoglobin levels between 70-90 and 80-100 g/L. Results: Lower minimum hemoglobin levels were associated with poor outcome on both univariate (p<0.001) and multivariate (p=0.012) analysis. Area under the ROC curve for minimum hemoglobin was 0.673. Youden index analysis found a minimum hemoglobin threshold of 91.5 g/L maximally predictive for good functional outcome. Propensity score matching showed a trend towards poor outcome in transfused patients with minimum hemoglobin levels between 70-90 and 80-100 g/L (p=0.052 and 0.09). Conclusions: This work suggests that decreasing hemoglobin is an independent predictor of poor outcome following aSAH. However, there was a trend towards poor outcome in transfused patients. The optimal transfusion threshold should be evaluated by prospective trials.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".