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Record W1990406538 · doi:10.1172/jci40839

Pathogenic antibodies are active participants in spinal cord injury

2009· letter· en· W1990406538 on OpenAlexaff
Gregory A. Dekaban, Sakina G. Thawer

Bibliographic record

VenueJournal of Clinical Investigation · 2009
Typeletter
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsNeuroprotectionPathogenesisAutoimmunitySpinal cord injuryMedicineAntibodySpinal cordImmunologyLesionDiseaseMechanism (biology)NeuroscienceBioinformaticsPathologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

The role of B cells and autoimmunity as contributing factors to poor neurological outcomes following spinal cord injury (SCI) is poorly understood. The study by Ankeny et al., in this issue of the JCI, identifies a new immunopathological mechanism arising after SCI in mice (see the related article beginning on page 2990). The study shows that B cells produce pathogenic antibodies that impair lesion repair, resulting in worse neurological outcome. This new understanding of SCI disease pathogenesis, if confirmed in humans, reveals potential avenues for the development of novel neuroprotective immunotherapies.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0130.005
Insufficient payload (model declined to judge)0.0030.002

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 head0.252
GPT teacher head0.431
Teacher spread0.179 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations21
Published2009
Admission routes1
Has abstractyes

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