The Use of Noninvasive Multimodal Neuromonitoring in Adult Critically Ill Patients With COVID-19 Infection
Bibliographic record
Abstract
INTRODUCTION: Noninvasive neuromonitoring could be a valuable option for bedside assessment of cerebral dysfunction in patients with coronavirus disease-2019 (COVID-19) admitted to intensive care units (ICUs). This systematic review aims to investigate the use of noninvasive multimodal neuromonitoring in critically ill adult patients with COVID-19 infection. METHODS: MEDLINE/PubMed, Scopus, Cochrane, and EMBASE databases were searched for studies investigating noninvasive neuromonitoring in patients with COVID-19 admitted to ICUs. The monitoring included transcranial Doppler ultrasonography (TCD), the Brain4care Corp. cerebral compliance monitor (B4C), optic nerve sheath diameter (ONSD), near infrared spectroscopy, automated pupillometry, and electroencephalography (EEG). RESULTS: Thirty-two studies that investigated noninvasive neuromonitoring techniques in patients with COVID-19 in the ICU were identified from a systematic search of 7001 articles: 1 study investigating TCD, ONSD and pupillometry; 2 studies investigating the B4C device and TCD; 3 studies investigating near infrared spectroscopy and TCD; 4 studies investigating TCD; 1 case series investigating pupillometry, and 21 studies investigating EEG. One hundred and nineteen patients underwent TCD monitoring, 47 pupillometry, 49 ONSD assessment, 50 compliance monitoring with the B4C device, and 900 EEG monitoring. Alterations in cerebral hemodynamics, brain compliance, brain oxygenation, pupillary response, and brain electrophysiological activity were common in patients with COVID-19 admitted to the ICU; these abnormalities were not clearly associated with worse outcome or the development of new neurological complications. CONCLUSIONS: The use of noninvasive multimodal neuromonitoring in critically ill COVID-19 patients could be considered to facilitate the detection of neurological derangements. Determining whether such findings allow earlier detection of neurological complications or guide appropriate therapy requires additional studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".