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Record W2807990765 · doi:10.1089/neu.2018.5935

A Review of Clinical Trials in Spinal Cord Injury Including Biomarkers

2018· review· en· W2807990765 on OpenAlexaff
Jetan H. Badhiwala, Jefferson R. Wilson, Brian K. Kwon, Steven Casha, Michael G. Fehlings

Bibliographic record

VenueJournal of Neurotrauma · 2018
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of CalgaryVancouver Spine Surgery InstituteUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsClinical trialMedicineSpinal cord injuryRandomized controlled trialIntensive care medicinePhysical therapySpinal cordSurgeryPathologyPsychiatry

Abstract

fetched live from OpenAlex

Acute traumatic spinal cord injury (SCI) entered the arena of prospective, randomized clinical trials almost 40 years ago, with the undertaking of the National Acute Spinal Cord Study (NASCIS) I trial. Since then, a number of clinical trials have been conducted in the field, spurred by the devastating physical, social, and economic consequences of acute SCI for patients, families, and society at large. Many of these have been controversial and attracted criticism. The current review provides a critical summary of select past and current clinical trials in SCI, focusing in particular on the findings of prospective, randomized controlled trials, the challenges and barriers encountered, and the valuable lessons learned that can be applied to future trials.

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)0.007
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.772
GPT teacher head0.674
Teacher spread0.097 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
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

Citations54
Published2018
Admission routes1
Has abstractyes

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