An Exploratory Study of the Bilateral Bispectral Index for Pain Detection in Traumatic-Brain-Injured Patients With Altered Level of Consciousness
Bibliographic record
Abstract
INTRODUCTION: Many patients with a traumatic brain injury (TBI) cannot communicate because of altered level of consciousness. Although observation of pain behaviors (e.g., frowning) is recommended for pain assessment in nonverbal populations, they are attenuated and sometimes even suppressed in patients with TBI receiving high doses of sedatives. This study explored the potential utility of the bilateral bispectral index system (BIS) for pain detection in critically ill adults with TBI and altered level of consciousness. METHODS: Using a repeated measure within-subject design, participants (N = 25) were observed for 1 minute before (baseline), during, and 15 minutes after two procedures: (a) noninvasive blood pressure (nonnociceptive) and (b) turning (nociceptive). At each assessment, BIS indexes (0-100) of the right (R) and left (L) hemispheres and pain behaviors were documented. RESULTS: Compared with baseline, significant median increases (p ≤ .05) in BIS-R (+4.93%) and BIS-L (+8.43%) and in the frequency of pain behaviors (+3.00) were observed during turning but not noninvasive blood pressure. Interestingly, increases in BIS-R were more pronounced in participants with left-sided TBI (+17.23%, p = .021) than those with right-sided TBI (+3.01%). BIS-R fluctuations in participants with left-sided TBI were also positively correlated (r(s) = .986, p ≤ .001) with the frequency of pain behaviors observed during turning. CONCLUSIONS: Overall, only increases in BIS-R were correlated with participants' pain behaviors and in those with left-sided TBI exclusively. Although further research is needed, our findings support the potential use of the bilateral BIS for pain detection in nonverbal patients with TBI who cannot behaviorally respond to pain, but only when they have a left-sided injury.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".