Cerebral Near‐Infrared Spectroscopy as a Measure of Nociceptive Evoked Activity in Critically III Infants
Bibliographic record
Abstract
Due to the subtlety or absence of predictable, objective signs of pain in critically ill infants, health care professionals must often rely on observations of behavioural or nonspecific physiological signals. Although parameters such as heart rate or blood pressure could be regarded as relatively more objective or quantifiable measures than behavioural signals, they are often unstable and generally nonspecific. However, reducing inaccuracies in pain assessment and misinterpretation of pain intensity may be facilitated by new techniques that shed light on the cerebral responses to pain that could be measured directly. Near‐infrared spectroscopy is one such technique that has been used to detect subtle changes in the concentrations of oxygenated and deoxygenated hemoglobin in the brains of preterm and term infants in response to stressful and/or painful stimuli. Following a review of cortical pain responses, this article provides an overview of near‐infrared spectroscopy technology and its use in functional activation studies in critically ill infants, and its potential applications in clinical settings and pain research. Signs of pain may be subtle or absent in a critically ill infant. The complex nature of pain may further obscure its identification and measurement. Because the use of monitoring and neuroimaging techniques has become more common in pain research, an understanding of these specialized technologies is important. Near‐infrared spectroscopy (NIRS) is a noninvasive technique for monitoring tissue hemodynamics and oxygenation. There are indications that NIRS is capable of detecting the cerebral hemodynamic changes associated with sensory stimuli, including pain, in infants. These developments suggest that NIRS may play an important role in research focusing on pain perception in critically ill infants. The present review briefly describes the cortical responses to noxious stimuli, which parallel cerebral hemodynamic responses to various stimuli. This is followed by an overview of NIRS technology including a summary of the literature on functional studies that have used NIRS in infants. Current NIRS techniques have well‐recognized limitations that must be considered carefully during the measurement and interpretation of the signals. Nonetheless, until more advanced NIRS techniques emerge, the current devices have strengths that should be exploited.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".