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Record W4322617484 · doi:10.1089/neu.2022.0407

Demographics, Mechanism of Injury, and Outcomes for Acute Upper and Lower Cervical Spinal Cord Injuries: An Analysis of 470 Patients in the Prospective, Multi-Center, North American Clinical Trials Network Registry

2023· article· en· W4322617484 on OpenAlexaff
Brittany Grace Futch, Romaric Waguia Kouam, Beatrice Ugiliweneza, James S. Harrop, Shekar N. Kurpad, Norah Foster, Khoi D. Than, Clifford Crutcher, C. Rory Goodwin, Charles H. Tator, Christopher I. Shaffrey, Bizhan Aarabi, Michael G. Fehlings, Chris J. Neal, James D. Guest, Muhammad M. Abd‐El‐Barr

Bibliographic record

VenueJournal of Neurotrauma · 2023
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineSpinal cord injuryClinical trialSpinal cordCervical vertebraeProspective cohort studyDemographicsSurgeryInternal medicine

Abstract

fetched live from OpenAlex

There is a paucity of data comparing the demographics, mechanism of injury, and outcomes of upper versus lower cervical spinal cord injuries (cSCI). The study objective was to define different clinical manifestations of cSCI. Data were collected prospectively through centers of the North American Clinical Trials Network (NACTN). Data was collected on 470 patients (21% women, mean age 50 years). Cervical vertebral level was analyzed as an ordinal variable to determine a natural demarcation to classify upper versus lower cSCI. For continuous variable analysis, falls were associated with C3 more than C4 vertebral level injuries (60% vs. 42%; p = 0.0126), while motor vehicle accidents were associated with C4 more than C3 (40% vs. 29%; p = 0.0962). Motor International Standards for Neurological Classification of Spinal Cord Injury scores also demonstrated a natural demarcation between C3 and C4, with C3 having higher median American Spinal Injury Association (ASIA) motor scores (40 [4-73] vs. 11 [3-59], p = 0.0227). There were no differences when comparing C2 to C3 nor C4 to C5. Given the significant differences seen between C3 and C4, but not C2 and C3 nor C4 and C5, upper cSCI was designated as C1-C3, and lower cSCI was designated as C4-C7. Compared with a lower cSCI, patients with an upper cSCI were more likely to have a fall as their mechanism of injury (54% vs. 36%; p = 0.0072). Patients with an ASIA C cSCI were likely to have an upper cervical injury: 23% vs. 11% ( p = 0.0226). Additionally, patients with an upper cSCI were more likely to have diabetes prior to injury: 37% versus 22%, respectively ( p = 0.0084). Lower cSCI were more likely injured through sports (19% vs. 8%, p = 0.0171) and present with ASIA A (42% vs. 25%, p = 0.0186) neurological grade. Patients with lower cSCI were also significantly more likely to have complications such as shock, pulmonary embolism, and pleural effusion. In conclusion, there appears to be a natural demarcation of injury type between C3 and C4. Upper cSCI (C1-C3) was more associated with falls and diabetes, whereas lower cSCI (C4-C7) was more associated with sports, worse ASIA scores, and more complications. Further research will be needed to understand the mechanistic and biological differences between these two groups and whether different treatments may be appropriate for each of these groups.

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)0.008
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.168
GPT teacher head0.505
Teacher spread0.337 · 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 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

Citations12
Published2023
Admission routes1
Has abstractyes

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