Intraventricular Fibrinolysis with Tissue Plasminogen Activator is Associated with Transient Cerebrospinal Fluid Inflammation: A Randomized Controlled Trial
Bibliographic record
Abstract
Locally administered tissue plasminogen activator (TPA) accelerates clearance of intraventricular hemorrhage (IVH), but its impact on neurologic outcomes remains unclear and preclinical research suggests it may have pro-inflammatory effects. We randomly allocated patients with ruptured cerebral aneurysms and IVH, treated with endovascular coiling and ventricular drainage, to receive either 2-mg intraventricular TPA or placebo every 12 hours. Cerebrospinal fluid (CSF) and serum cytokine and white blood cell (WBC) concentrations were measured before drug administration and daily for 72 hours. Cerebrospinal fluid D-dimer levels were assessed 6 and 12 hours after administration to quantify fibrinolysis. Six patients were randomized to each group. Patients treated with TPA developed higher CSF cytokine concentrations compared with placebo-treated patients (P<0.05 for tumor necrosis factor-α, interferon-γ, interleukin (IL)-1α, IL-1β, IL-2, IL-4, and IL-6), as well as higher CSF WBC counts (P=0.03). Differences were greatest after 24 hours and decreased over 48 to 72 hours. The magnitude of the inflammatory response was significantly associated with peak CSF D-dimer concentration and extent of IVH clearance. We conclude that intraventricular TPA administration produces a transient local inflammatory response, the severity of which is strongly associated with the degree of fibrinolysis, suggesting it may be induced by release of hematoma breakdown products, rather than the drug itself.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".