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Record W4367053140 · doi:10.1089/neu.2022.0507

Variability in Early Surgery for Acute Cervical Spinal Cord Injury Patients: An Opportunity for Enhanced Care Delivery

2023· article· en· W4367053140 on OpenAlexaff
Chris J. Neal, Beatrice Ugiliweneza, Elizabeth G. Toups, Muhammad M. Abd‐El‐Barr, George Jimsheleishvili, Shekar N. Kurpad, Bizhan Aarabi, James S. Harrop, Christopher I. Shaffrey, Michael G. Fehlings, Charles H. Tator, Robert G. Grossman, James D. Guest

Bibliographic record

VenueJournal of Neurotrauma · 2023
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineSpinal cord injuryClinical trialSurgeryAnesthesiaSpinal cordInternal medicine

Abstract

fetched live from OpenAlex

Data supporting the benefits of early surgical intervention in acute spinal cord injury (SCI) is growing. For early surgery to be accomplished, understanding the causes of variabilities that effect the timing of surgery is needed to achieve this goal. The purpose of this analysis is to determine factors that affect the timing of surgery for acute cervical SCI within the North American Clinical Trials Network (NACTN) for SCI registry. Patients in the NACTN SCI registry from 2005 to 2019 with a cervical SCI, excluding acute traumatic central cord syndrome, were analyzed for time elapsed from injury to arrival to the hospital, and time to surgery. Two categories were defined: 1) Early Arrival with Early Surgery (EAES) commenced within 24 h of injury, and 2) Early Arrival but Delayed Surgery (EADS), with surgery occurring between 24 to 72 h post-injury. Patients' demographic features, initial clinical evaluation, medical comorbidities, neurological status, surgical intervention, complications, and outcome data were correlated with respect to the two arrival groups. Of the 222 acute cervical SCI patients undergoing surgery, 163 (73.4%) were EAES, and 59 (26.6%) were EADS. There was no statistical difference in arrival time between the EAES and EADS groups. There was a statistical difference in the median arrival time to surgery between the EAES group (9 h) compared with the EADS group (31 h; p < 0.05). There was no statistical difference in race, sex, age, mechanism of injury, Acute Physiologic Assessment and Chronic Health Evaluation (APACHE) II scores, or medical comorbidities between the two groups, but the EAES group did present with a significantly lower systolic blood pressure ( p < 0.05). EADS patients were more likely to present as an American Spinal Injury Association Impairment Scale (AIS) D than EAES ( p < 0.05). Early surgery was statistically more likely to occur if the injury occurred over the weekend ( p < 0.05). There were variations in the rates of early surgery between the eight NACTN sites within the study, ranging from 57 to 100%. Of the 114 patients with 6-month outcome data, there was no significant change between the two groups regarding AIS grade change and motor/pin prick/light touch score recovery. A trend towards improved motor scores with early surgery was not statistically significant ( p = 0.21). Although there is data that surgery within 24 h of injury improves outcomes and can be performed safely, there remain variations in care outside of clinical trials. In the present study of cervical SCI, NACTN achieved its goal of early surgery in 73.4% of patients from 2005-2019 who arrived within 24 h of their injury. Variability in achieving this goal was related to severity of neurological injury, the day of the week, and the treating NACTN center. Evaluating variations within our network improves understanding of potential systemic limitations and our decision-making process to accomplish the goal of early surgery.

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)0.009
metaresearch head score (Gemma)0.044
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 score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.167
GPT teacher head0.444
Teacher spread0.277 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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