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Record W2163018412 · doi:10.1177/0885066608318458

Cytokines and Brain Injury: Invited Review

2008· review· en· W2163018412 on OpenAlex
Hazim Kadhim, Jean Duchateau, Guillaume Sébire

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

VenueJournal of Intensive Care Medicine · 2008
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsHôpital FleurimontUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineNeuroprotectionNeuroscienceInflammationImmune systemBrain damageCytokineImmunologyPharmacologyPsychology

Abstract

fetched live from OpenAlex

The brain reacts to injury or disease by cascades of cellular and molecular responses. Evidence suggests that immune-inflammatory processes are key elements in the physiopathological processes associated with brain injury or damage. Cytokines are among major mediators implicated in these processes. Cytokine responses in the initial phase of brain injury might have a role in aggravating brain damage. However, in later stages, these molecular mediators might contribute to recovery or repair. Hemodynamic stabilization and optimalization of oxygen delivery to the brain remain cornerstones in the management of acute brain injury. New approaches might use anticytokine therapy to limit progression and halt or attenuate secondary brain damage. Progress toward such novel neuroprotection strategies, however, awaits better understanding of the optimal timing and dosing of those neuromodulatory therapies and better knowledge of the numerous interactions of those mediators. This also requires understanding of how and when precisely immune mechanisms shift from noxious to protective or restorative actions.

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)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.093
GPT teacher head0.375
Teacher spread0.283 · 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