Traumatic Brain Injury and Neurovascular Disturbances
Abstract
Background
AND
Objectives
Penetrating brain injury (PBI) is a highly lethal form of traumatic brain injury, with substantial mortality and marked differences across high-income countries and low- to middle-income countries. The aim of the Comparative Effectiveness Research in the Americas on Penetrating Brain Injury (COMPAS-PBI) study was to evaluate management variability, compare outcomes across resource settings, and assess the feasibility of real-time multinational neurocritical care data collection.
Methods
COMPAS-PBI is a multinational, multicenter observational comparative effectiveness study. It includes a retrospective cohort (2021-2025) and a prospective cohort (2026-2028) of adults aged 18 years or older with confirmed PBI, defined by radiographic evidence of dural penetration on head computed tomography, presenting within 24 hours of injury. Eleven trauma centers across 8 countries in the Americas will participate, with the University of Oklahoma Health Sciences Campus serving as the coordinating center. The principal management strategies compared are intracranial pressure monitoring vs no monitoring and neurosurgical intervention, primarily early decompressive craniectomy, vs medical management alone. Comparative analyses will use propensity score-based methods to address baseline confounding. EXPECTED OUTCOMES: The primary outcome is in-hospital mortality. Secondary outcomes include disposition at discharge, Glasgow Outcome Scale-Extended score (at discharge, 6, and 12 months), and timing of intracranial pressure monitoring and decompressive craniectomy. Feasibility outcomes will assess completeness, timeliness, and quality of prospective real-time data capture, particularly in resource-limited settings.
Discussion
COMPAS-PBI will generate the first multinational comparative effectiveness data sets focused on civilian PBI within the Americas. The study is expected to provide pragmatic evidence to inform context-sensitive care strategies, reduce practice differences, and support future collaborative neurotrauma research.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
It is proved that any global minimizer of the trajectory balance objective can define a policy that samples exactly from the target distribution, and empirically demonstrate the benefits of the trajectories balance objective for GFlowNet convergence, diversity of generated samples, and robustness to long action sequenc...
Esmeralda S. Whitammer, Moksh Jain, Emmanuel Bengio et al.· Neural Information Processin...· 302 citations· ⚡60
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.