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Record W2159006320 · doi:10.1089/neu.2013.3278

Minimizing Errors in Acute Traumatic Spinal Cord Injury Trials by Acknowledging the Heterogeneity of Spinal Cord Anatomy and Injury Severity: An Observational Canadian Cohort Analysis

2014· article· en· W2159006320 on OpenAlexaffabout
Marcel F. Dvorak, Vanessa K. Noonan, Nader Fallah, Charles G. Fisher, Carly S. Rivers, Henry Ahn, Eve C. Tsai, Gary Linassi, Sean Christie, Najmedden Attabib, R. John Hurlbert, Daryl R. Fourney, Michael G. Johnson, Michael G. Fehlings, Brian Drew, Christopher S. Bailey, Jérôme Paquet, Stefan Parent, Andrea Townson, Chester Ho, B. Catharine Craven, Dany H. Gagnon, Deborah Tsui, Richard Fox, Jean‐Marc Mac‐Thiong, Brian K. Kwon

Bibliographic record

VenueJournal of Neurotrauma · 2014
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsToronto Rehabilitation InstituteUniversité LavalMcMaster UniversityHamilton General HospitalPublic Health OntarioHôpital de l'Enfant-JésusUniversity of ManitobaUniversity of CalgaryUniversity of British ColumbiaSaint John Regional HospitalUniversity Health NetworkCentre Hospitalier Universitaire Sainte-JustineOttawa HospitalDalhousie UniversityUniversity of OttawaRoyal Alexandra HospitalHamilton Health SciencesWestern UniversityUniversity of TorontoSt. Michael's HospitalUniversité de MontréalUniversity of SaskatchewanPraxis Spinal Cord InstituteHorizon Health Network
Fundersnot available
KeywordsMedicineSpinal cord injuryClinical trialInjury Severity ScoreObservational studyCohort studyPhysical therapyCohortAbbreviated Injury ScalePhysical medicine and rehabilitationSpinal cordRandomized controlled trialPoison controlInjury preventionSurgeryInternal medicineEmergency medicine

Abstract

fetched live from OpenAlex

Clinical trials of therapies for acute traumatic spinal cord injury (tSCI) have failed to convincingly demonstrate efficacy in improving neurologic function. Failing to acknowledge the heterogeneity of these injuries and under-appreciating the impact of the most important baseline prognostic variables likely contributes to this translational failure. Our hypothesis was that neurological level and severity of initial injury (measured by the American Spinal Injury Association Impairment Scale [AIS]) act jointly and are the major determinants of motor recovery. Our objective was to quantify the influence of these variables when considered together on early motor score recovery following acute tSCI. Eight hundred thirty-six participants from the Rick Hansen Spinal Cord Injury Registry were analyzed for motor score improvement from baseline to follow-up. In AIS A, B, and C patients, cervical and thoracic injuries displayed significantly different motor score recovery. AIS A patients with thoracic (T2-T10) and thoracolumbar (T11-L2) injuries had significantly different motor improvement. High (C1-C4) and low (C5-T1) cervical injuries demonstrated differences in upper extremity motor recovery in AIS B, C, and D. A hypothetical clinical trial example demonstrated the benefits of stratifying on neurological level and severity of injury. Clinically meaningful motor score recovery is predictably related to the neurological level of injury and the severity of the baseline neurological impairment. Stratifying clinical trial cohorts using a joint distribution of these two variables will enhance a study's chance of identifying a true treatment effect and minimize the risk of misattributed treatment effects. Clinical studies should stratify participants based on these factors and record the number of participants and their mean baseline motor scores for each category of this joint distribution as part of the reporting of participant characteristics. Improved clinical trial design is a high priority as new therapies and interventions for tSCI emerge.

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)0.274
metaresearch head score (Gemma)0.433
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.983

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2740.433
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.009
Bibliometrics0.0040.008
Science and technology studies0.0040.003
Scholarly communication0.0050.002
Open science0.0050.003
Research integrity0.0030.003
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 head0.210
GPT teacher head0.466
Teacher spread0.256 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
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

Citations87
Published2014
Admission routes2
Has abstractyes

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