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Record W3166881147 · doi:10.1101/2021.05.28.21257947

Pharmacological Management of Acute Spinal Cord Injury: A longitudinal multi-cohort observational study

2021· preprint· en· W3166881147 on OpenAlexaff
Catherine R. Jutzeler, Lucie Bourguignon, Bobo Tong, Elias Ronca, Eric Bailey, Noam Y. Harel, Fred H. Geisler, Adam R. Ferguson, Brian K. Kwon, Jacquelyn J. Cragg, Lukas Grassner, John L. K. Kramer

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsVancouver Coastal HealthUniversity of SaskatchewanInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersWings for LifeSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsMedicineSpinal cord injuryObservational studyCohortCohort studyConcomitantClinical trialSpinal cordAnesthesiaEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT Background Nearly every individual sustaining traumatic spinal cord injury receives multiple types and classes of medications to manage a litany of secondary complications. Prior clinical studies and evidence from animal models suggest that several of these medications could enhance or impede endogenous neurological recovery. However, there is a knowledge gap surrounding the spectrum of pharmacologic agents typically administered in the routine management of spinal cord injury. Objective To systematically determine the types of medications commonly administered, alone or in combination, in the acute to subacute phase of spinal cord injury. Methods We conducted an analysis of two largescale cohorts (the Sygen interventional trial and the SCIRehab observational cohort study) to determine what constitutes “ standards of acute pharmacological care” after spinal cord injury. Concomitant medication use, including dosage, timing and reason for administration, was tracked. Descriptive statistics were used to describe the medications administered within the first 60 days after spinal cord injury. Results Across 2040 individuals with spinal cord injury, 775 unique medications were administered within the two months after injury. On average, patients enrolled in the Sygen trial received 9.9 ± 4.9 (range 0-34), 14.3 ± 6.3 (range 1-40), 18.6 ± 8.2 (range 0-58), and 21.5 ± 9.7 (range 0-59) medications within the first 7, 14, 30, and 60 days post-injury, respectively. Patients enrolled in the SCIRehab cohort study received on average 1.7 ± 1.7 (range 0-11), 3.7 ± 3.7 (range 0-24), 8.5 ± 6.3 (range 0-42), and 13.5 ± 8.3 (range 0-52) medications within the first 7, 14, 30, and 60 days post-injury, respectively. Polypharmacy was commonplace (up to 43 medications per day per patient). Approximately 10% of medications were administered acutely as prophylaxis (e.g., against the development of pain or infections). Conclusions To our knowledge, this was the first time acute pharmacological practices have been comprehensively examined after spinal cord injury. Our study revealed a high degree of polypharmacy in the acute stages of spinal cord injury, with potential to both positively and negatively impact neurological recovery. This data may provide key insight to achieve better understanding of how the acute pharmacological management of spinal cord injury affects long-term recovery. All results can be interactively explored on the R X SCI web site ( https://jutzelec.shinyapps.io/RxSCI/ ) and GitHub repository ( https://github.com/jutzca/Acute-Pharmacological-Treatment-in-SCI/ ).

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)0.003
metaresearch head score (Gemma)0.005
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 score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.308
GPT teacher head0.503
Teacher spread0.194 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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