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Record W2110932878 · doi:10.1177/107385840200800108

Book Review: Recruiting the Immune Response to Promote Axon Regeneration in the Injured Spinal Cord

2002· review· en· W2110932878 on OpenAlexaff
Samuel David, Shalina S. Ousman

Bibliographic record

VenueThe Neuroscientist · 2002
Typereview
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsRegeneration (biology)AxonNeuroscienceImmune systemSpinal cord injurySpinal cordCentral nervous systemMyelinBiologyMedicineImmunologyCell biology

Abstract

fetched live from OpenAlex

Myelin contains molecules that can inhibit the growth and regeneration of axons. Neutralizing the activity of these inhibitors can enhance axon regeneration in the adult mammalian central nervous system (CNS). The complexity of the CNS-immune system interactions after CNS trauma is now beginning to be better understood. Recent studies indicate that both cell-mediated and antibody-mediated immune responses can help in promoting axon regeneration after CNS injury. It is hoped that such advances will lead to the development of safe and effective vaccine and cytokine treatments for spinal cord injuries.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.022

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 head0.114
GPT teacher head0.376
Teacher spread0.262 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations34
Published2002
Admission routes1
Has abstractyes

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