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Record W2418819465 · doi:10.1051/medsci/20153111012

La neuro-inflammation

2015· review· fr· W2418819465 on OpenAlex
Justine Renaud, Hélène-Marie Thérien, Marilyn Plouffe, Maria‐Grazia Martinoli

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

Venuemédecine/sciences · 2015
Typereview
Languagefr
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsCentre hospitalier de l'Université LavalUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsNeuroinflammationNeuroscienceCrosstalkCentral nervous systemInflammationImmune systemMicrogliaHomeostasisBiologyMedicineImmunologyCell biologyEngineering

Abstract

fetched live from OpenAlex

Sheltered in a bony cage, populated by cells with little regenerative potential, the central nervous system (CNS) could likely not withstand classic inflammation without risking major sequelae. As a consequence, it had to develop an original way to provide surveillance, defence and reparation, which relies on both the complex architecture of the periphery-nervous parenchyma exchange zones, and the tightly regulated collaboration between all the cell populations that reside in or pass through the CNS. Despite its tight regulation, neuroinflammation is sometimes the cause of irreversible loss but it is also where the solution stands. The specific immune crosstalk that takes place in the CNS needs to be decoded in order to identify the best therapeutic strategies aimed at helping the CNS to restore homeostasis in problematic situations, such as in the case of neurodegenerative disorders. This review deals with this double-edged sword nature of neuroinflammation.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.007

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 head0.141
GPT teacher head0.360
Teacher spread0.220 · 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