The Use of the Bispectral Index in the Detection of Pain in Mechanically Ventilated Adults in the Intensive Care Unit: A Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Pain assessment is an immense challenge for clinicians, especially in the context of the intensive care unit, where the patient is often unable to communicate verbally. Several methods of pain assessment have been proposed to assess pain in this environment. These include both behavioural observation scales and evaluation of physiological measurements such as heart rate and blood pressure. Although numerous validation studies pertaining to behavioural observation scales have been published, several limitations associated with using these measures for pain assessment remain. Over the past few years, researchers have been interested in the use of the bispectral index monitoring system as a proxy for the evaluation of encephalography readings to assess the level of anesthesia and, potentially, analgesia. OBJECTIVES: To synthesize the main studies exploring the use of the bispectral index monitoring system for pain assessment, to guide future research in adults under sedation in the intensive care unit. METHOD: The EMBASE, Medline, CINAHL and PsycINFO databases were searched for studies published between 1996 and 2013 that evaluated the use of the bispectral index in assessing pain. RESULTS: Most studies conclude that nociceptive stimulation causes a significant increase in the bispectral index and revealed the importance of controlling certain confounding variables such as the level of sedation. DISCUSSION: Further studies are needed to clearly demonstrate the relationship between nociceptive stimuli and the bispectral index, as well as the specificity of the bispectral index in detecting pain.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".