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Record W2472715975 · doi:10.1051/medsci/20163206022

Inflammation et régénération musculaire

2016· review· fr· W2472715975 on OpenAlex
Sébastien S. Dufresne, Jérôme Frenette, Nicolas A. Dumont

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 · 2016
Typereview
Languagefr
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversité LavalCentre Hospitalier Universitaire Sainte-JustineCentre hospitalier de l'Université LavalUniversité de MontréalCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsInflammationRegeneration (biology)Inflammatory responseImmunologyMedicinePhenotypeFunction (biology)Muscle tissueCell biologyBiologyAnatomyBiochemistry

Abstract

fetched live from OpenAlex

Muscle injuries are very frequent and are associated with an inflammatory reaction that varies in intensity. Classically the inflammatory process was considered harmful for muscle regeneration and anti-inflammatory agents are still part of a conventional therapy. Over the last decades, it has been demonstrated under some conditions that the inflammatory response could be detrimental for the musculoskeletal tissue. However, accumulating evidence indicate that controlled and efficient inflammatory response is necessary for an optimal muscle recovery. Among the resident and infiltrating leukocytes that participate into the inflammatory process, macrophages play a critical role in muscle regeneration due to their ability to switch from pro-inflammatory to anti-inflammatory phenotypes depending on their microenvironment. The present review synthesizes the recent advances regarding the interactions of the different infiltrating and resident leukocytes on myogenic cell function and muscle regeneration.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.030
GPT teacher head0.331
Teacher spread0.301 · 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