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Record W2015417431 · doi:10.2174/1570159033477008

Brain Inflammation Following Intracerebral Hemorrhage

2003· article· en· W2015417431 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Neuropharmacology · 2003
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsInflammationMedicineIntracerebral hemorrhageStroke (engine)CoagulopathyPathogenesisSystemic inflammationImmunologyAnesthesiaInternal medicineSubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

Approximately 15% of cerebral strokes in adults are due to bleeding into the brain (intracerebral hemorrhage, ICH). This can be related to hypertension, vascular anomalies, or coagulopathy. Prognosis following ICH is worse than that following ischemic stroke. In addition, head trauma and premature birth are associated with ICH. Inflammation occurs after ICH and might be an important part of the pathogenesis of brain damage. The goal of this review is to bring together recent diverse data concerning inflammation after ICH. There has been little investigation of the role of inflammation following ICH despite the fact that inflammation is more severe than in ischemic stroke. Inflammation in the brain follows a temporal sequence similar to that in other organs. Some cytokines and inflammatory cells may possess dual roles both deleterious and beneficial to brain after ICH. At present, experimental data only weakly support pursuit of pharmacologic anti-inflammatory strategies following ICH. Keywords: inflammation, cytokines, adhesion molecules, leukocytes, ich, brain trauma, animal models, anti-inflammation

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.029
GPT teacher head0.346
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it