Inflammatory and metalloproteinases profiles predict three-month poor outcomes in ischemic stroke treated with thrombolysis
Bibliographic record
Abstract
Inflammatory mediators and metalloproteinases are altered in acute ischemic stroke (AIS) and play a detrimental effect on clinical severity and hemorrhagic transformation of the ischemic brain lesion. Using data from the Italian multicenter observational MAGIC (MArker bioloGici nell'Ictus Cerebrale) Study, we evaluated the effect of inflammatory and metalloproteinases profiles on three-month functional outcome, hemorrhagic transformation and mortality in 327 patients with AIS treated with intravenous thrombolys in according to SITS-MOST (Safe Implementation of Thrombolysis in Stroke-MOnitoring STudy) criteria. Circulating biomarkers were assessed at baseline and 24 h after thrombolysis. Adjusting for age, sex, baseline glycemia and National Institute of Health Stroke Scale, history of atrial fibrillation or congestive heart failure, and of inflammatory diseases or infections, baseline alpha-2macroglobulin (A2M), baseline serum amyloid protein (SAP) and pre-post tissue-plasminogen activator (tPA) variations (Δ) of metalloproteinase 9, remained significantly and independently associated with three-month death [OR (95% CI):A2M:2.99 (1.19-7.53); SAP:5.46 (1.64-18.74); Δmetalloproteinase 9:1.60 (1.12-2.27)]. The addition of baseline A2M and Δmetalloproteinase 9 or baseline SAP and Δmetalloproteinase 9 (model-2 or model-3) to clinical variables (model-1) significantly improved the area under curve for prediction of death [model-2 with A2M: p = 0.0205; model-3 with SAP: p = 0.001]. In conclusion, among AIS patients treated with thrombolysis, circulating A2M, SAP and Δmetalloproteinase 9 are independent markers of poor outcome. These results may prompt controlled clinical research about agents antagonizing their effect.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".