Early Elevation of Serum Tumor Necrosis Factor-α is Associated with Poor Outcome in Subarachnoid Hemorrhage
Bibliographic record
Abstract
OBJECTIVE: Subarachnoid hemorrhage (SAH) is associated with inflammation that may mediate poor outcome in SAH. We hypothesize that elevated serum tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6) are associated with vasospasm and poor outcome in SAH. METHODS: In 52 consecutive SAH subjects, we compared TNF-α and IL-6 levels on post-SAH days 0 to 1, 2 to 3, 4 to 5, 6 to 8, and 10 to 14 with respect to vasospasm and to poor outcome at 3 and 6 months. Vasospasm was defined as more than 50% reduction in vessel caliber on angiography. Poor outcome was defined as modified Rankin score greater than 2. RESULTS: Elevated TNF-α on post-SAH days 2 to 3 was associated with poor 3-month outcome (P = 0.0004). Global elevation of TNF-α over time (post-SAH days 0-14) was independently associated with poor 3-month outcome after adjusting for Hunt-and-Hess grade and age (P = 0.02). Neither cross-sectional nor IL-6 levels over time were associated with outcome. Neither TNF-α nor IL-6 levels were associated with vasospasm. CONCLUSIONS: Elevation in serum TNF-α on post-SAH days 2 to 3 and global elevation of TNF-α over time are associated with poor outcome but not with angiographic vasospasm in this small cohort. Future studies are needed to define the role of TNF-α in SAH-related brain injury and its potential as a SAH outcome biomarker.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".