Non-Invasive Techniques for Multimodal Monitoring in Traumatic Brain Injury: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Monitoring brain oxygenation and intracranial pressure non-invasively and continuously is of paramount importance in traumatic brain injury (TBI). The primary motivation of this study was to identify and provide robust evidence of the most effective techniques for the non-invasive multimodal monitoring for traumatic brain injury. Two reviewers independently searched PubMed, Embase, Scopus, the Cochrane Library, and the Web of Science between January 15, 2010, and January 22, 2020. Cohort studies assessing correlation or accuracy of non-invasive techniques for intracranial pressure (ICP) and/or brain oxygenation monitoring in TBI patients were included. The Newcastle–Ottawa Scale was used to assess the methodological quality of the studies. PROSPERO registration ID is CRD42020164739. Eight out of the 12 studies selected focused on the non-invasive measurement of ICP. Near-Infrared spectroscopy was the main technology for brain oxygenation, whereas ultrasound-based techniques were also used for ICP monitoring. PbtO 2 monitoring through near-infrared spectroscopy showed low correlation and limited accuracy in detecting hypoxic events. A meta-analysis on non-invasive ICP monitoring revealed a strong pooled correlation coefficient of 0.725 (95 % confidence interval [CI]: 0.450–0.874; I 2 91.31%) between transcranial Doppler and the gold standard ICP monitoring. The current meta-analysis has shown that the two most prominent and widely used technologies for non-invasive monitoring in TBI are near-infrared spectroscopy and transcranial Doppler. Both techniques could be considered for the future development of a single non-invasive and continuous multimodal monitoring device for TBI.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".