MétaCan
Menu
Back to cohort
Record W2587810758 · doi:10.1016/j.celrep.2017.01.048

Early Administration of Gabapentinoids Improves Motor Recovery after Human Spinal Cord Injury

2017· article· en· W2587810758 on OpenAlexafffund
Freda M. Warner, Jacquelyn J. Cragg, Catherine R. Jutzeler, Frank Röhrich, Norbert Weidner, Marion Saur, Doris Maier, Christian Schuld, Armin Curt, John K. G. Kramer

Bibliographic record

VenueCell Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersUniversity of British ColumbiaInternational Foundation for Research in ParaplegiaMichael Smith Health Research BCWings for Life
KeywordsMedicineAnticonvulsantSpinal cord injuryPregabalinAnesthesiaSpinal cordMotor functionEpilepsyPhysical medicine and rehabilitationPsychiatry

Abstract

fetched live from OpenAlex

The anticonvulsant pregabalin promotes neural regeneration in a mouse model of spinal cord injury (SCI). We have also previously observed that anticonvulsants improve motor outcomes following human SCI. The present study examined the optimal timing and type of anticonvulsants administered in a large, prospective, multi-center, cohort study in acute SCI. Mixed-effects regression techniques were used to model total motor scores at 1, 3, 6, and 12 months post injury. We found that early (not late) administration of anticonvulsants significantly improved motor recovery (6.25 points over 1 year). The beneficial effect of anticonvulsants remained significant after adjustment for differences in 1-month motor scores and injury characteristics. A review of a subset of patients revealed that gabapentinoids were the most frequently administrated anticonvulsant. Together with preclinical findings, intervention with anticonvulsants represents a potential pharmacological strategy to improve motor function after SCI.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.379
Teacher spread0.341 · 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 teacher head, 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

Citations58
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueCell ReportsSame topicSpinal Cord Injury ResearchFrench-language works237,207