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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 OpenAlex
LisaM Bond, Lisa McKerracher

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.010
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0010.003

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