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Record W4415953767 · doi:10.1177/08977151251392895

Funding Distributions, Trends, Gaps, and Policy Implications for Spinal Cord Injury Research: A Systematic Analysis of U.S. Federal Funds

2025· article· en· W4415953767 on OpenAlexafffund
Tucker Gillespie, Andrew Buxton, Bethany R. Kondiles, Miranda E. Leal-Garcia, Ashley V. Tran, K.N.C. Vo, Lucy Abu, Tanya A. Barretto, Jason Biundo, Sam Duenwald, Abigail Evans, Timothy N. Friedman, Isabella Gadaleta, Bryson Gottschall, Peyton Green, Grant Lee, Lilian Liu, Raza N. Malik, Chiara Sorani, Hannah Thomas, Christopher Barr, Ian Burkhart, Dylan A. McCreedy, Peter C. Nowell̀, Heath Blackmon, Alexander G. Rabchevsky, Matthew Rodreick, Abel Torres‐Espín, Jennifer N. Dulin

Bibliographic record

VenueJournal of Neurotrauma · 2025
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of WaterlooInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersNational Science Foundation Graduate Research Fellowship ProgramCongressionally Directed Medical Research ProgramsNational Institute of Neurological Disorders and StrokeCraig H. Neilsen FoundationMichael Smith Health Research BCParalyzed Veterans of AmericaU.S. Department of DefenseWorld Health OrganizationU.S. Department of Veterans AffairsParalyzed Veterans of America Research FoundationNational Institutes of HealthNational Science Foundation
KeywordsSpinal cord injuryTranslational researchVeterans AffairsPsychological interventionIntervention (counseling)Grant fundingRehabilitationMental healthGovernment (linguistics)Federal budget

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.057
metaresearch head score (Gemma)0.189
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0570.189
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0220.043
Science and technology studies0.0020.002
Scholarly communication0.0040.005
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.306
GPT teacher head0.548
Teacher spread0.242 · 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.

Study designObservational
DomainIncentives
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

Citations0
Published2025
Admission routes2
Has abstractyes

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