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Record W4415556142 · doi:10.1016/j.spinee.2025.10.030

Disparities in NIH funding for acute traumatic and chronic nontraumatic spinal cord injury: a 10-year national analysis

2025· article· en· W4415556142 on OpenAlexaff
Pushpinder Dhillon, Brian F. Saway, Audrey Galimba, Kyle P. Stegmann, Noah Nawabi, Yi Lü, Rajiv Saigal, Konstantinos Margetis, Michael G. Fehlings

Bibliographic record

VenueThe Spine Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Toronto
FundersNational Institutes of Health
KeywordsSpinal cord injurySpinal cordSpinal cord compressionCord

Abstract

fetched live from OpenAlex

Spinal cord injuries (SCI) are severe neurological conditions that lead to significant motor, sensory, and autonomic impairments. Critical research that addresses SCI encompasses both acute-traumatic and chronic non-traumatic SCI. Acute-traumatic SCI results from trauma to the spinal cord—where the injury occurs rapidly—and includes the long-term sequelae of the immediate injury. By contrast, chronic non-traumatic SCI results from spondylotic or oncologic processes that lead to prolonged, progressive spinal cord compression and ischemia leading to myelopathy.[1] Understanding both forms of SCI is critical to improving patient outcomes; however, patterns in research funding allocation may reveal broader priorities and potential imbalances within the scientific and medical communities.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.449
Teacher spread0.384 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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