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Record W4387220322 · doi:10.3171/2023.8.peds23354

Risk factors associated with in-hospital adverse events: a multicenter observational cohort study of 1853 pediatric patients with traumatic spinal cord injury

2023· article· en· W4387220322 on OpenAlexaff
Armaan K. Malhotra, Christopher S. Lozano, Husain Shakil, Christopher W. Smith, George M. Ibrahim, David E. Lebel, Abhaya V. Kulkarni, Jefferson R. Wilson, Christopher D. Witiw, Avery B. Nathens

Bibliographic record

VenueJournal of Neurosurgery Pediatrics · 2023
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreHospital for Sick ChildrenUniversity of TorontoSickKids FoundationInstitute for Work & HealthSt. Michael's Hospital
Fundersnot available
KeywordsMedicineSpinal cord injuryEmergency medicineCohortCohort studyInjury Severity ScoreAdverse effectConfoundingLogistic regressionPoison controlInternal medicineInjury preventionSpinal cord

Abstract

fetched live from OpenAlex

OBJECTIVE: In this study, the authors aimed to quantify the frequency of in-hospital major adverse events (AEs) in a multicenter cohort of pediatric patients with spinal cord injury (SCI) managed at North American trauma centers. They also sought to identify patient and injury factors associated with the occurrence of major and immobility-related AEs. METHODS: Data derived from the American College of Surgeons (ACS) Trauma Quality Improvement Program (TQIP) were used to identify a cohort of pediatric patients (age < 19 years) with traumatic SCI. The authors identified individuals with major and immobility-related AEs following injury. They used mixed-effects multivariable logistic regression to identify clinical variables associated with AEs after injury. This analytical approach allowed them to account for similarities in care delivery between patients managed in the same trauma settings during the study period while also adjusting for patient-level confounders. The adjusted impact of AEs on in-hospital mortality and length of stay (LOS) were also assessed through further multivariable regression analysis. Additional subgroup analyses were performed to reduce bias associated with competing risks and explore the age-specific risk factor associations with AEs. RESULTS: A total of 1853 pediatric patients who sustained either cervical or thoracic SCI were managed at ACS TQIP trauma centers between 2017 and 2020. The most frequently encountered AE types were pressure ulcer, unplanned intubation, cardiac arrest requiring cardiopulmonary resuscitation, and ventilator-associated pneumonia. The crude rate of major in-hospital and immobility-related AEs significantly differed between subgroups, with higher proportions of AEs in complete injuries compared with incomplete injuries. The adjusted risk for major AE following injury was significantly elevated for cervical complete SCI, patients with severe concomitant abdominal injuries, and for those presenting with depressed Glasgow Coma Scale scores less than 13. These same risk factors were associated with major AEs in children older than 8 years but were not significant for younger children (age ≤ 8 years). Complication occurrence was not associated with difference in risk-adjusted mortality (OR 0.72, 95% CI 0.45-1.14), but did increase LOS by 2.2 days (95% CI 1.4-2.7 days). CONCLUSIONS: The authors outlined the prevalence of in-hospital AEs in a large multicenter cohort of North American pediatric SCI patients. Important risk factors predisposing this population to AEs include cervical complete injuries, simultaneous abdominal trauma, and Glasgow Coma Scale scores < 13 at presentation. Postinjury complications impacted health resource utilization by increased LOS but did not affect postinjury mortality. These findings have important implications for pediatric SCI providers and future care quality benchmarking.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.001
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 head0.056
GPT teacher head0.337
Teacher spread0.281 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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