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Record W2001871254 · doi:10.4103/1673-5374.139470

Cervical spinal cord injury: tailoring clinical trial endpoints to reflect meaningful functional improvements

2014· review· en· W2001871254 on OpenAlexaff
LisaM Bond, Lisa McKerracher

Bibliographic record

VenueNeural Regeneration Research · 2014
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineClinical endpointClinical trialSpinal cord injuryPhysical medicine and rehabilitationCervical vertebraePhysical therapySpinal cordSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Cervical spinal cord injury (SCI) results in partial to full paralysis of the upper and lower extremities. Traditional primary endpoints for acute SCI clinical trials are too broad to assess functional recovery in cervical subjects, raising the possibility of false positive outcomes in trials for cervical SCI. Endpoints focused on the recovery of hand and arm control (e.g., upper extremity motor score, motor level change) show the most potential for use as primary outcomes in upcoming trials of cervical SCI. As the field moves forward, the most reliable way to ensure meaningful clinical testing in cervical subjects may be the development of a composite primary endpoint that measures both neurological recovery and functional improvement.

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)0.011
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.611
GPT teacher head0.623
Teacher spread0.012 · 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 designTheoretical or conceptual
DomainMethods
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

Citations8
Published2014
Admission routes1
Has abstractyes

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