Can Fluctuations in Vital Signs Be Used for Pain Assessment in Critically Ill Patients with a Traumatic Brain Injury?
Bibliographic record
Abstract
Background. Many critically ill patients with a traumatic brain injury (TBI) are unable to communicate. While observation of behaviors is recommended for pain assessment in nonverbal populations, they are undetectable in TBI patients who are under the effects of neuroblocking agents. Aim. This study aimed to validate the use of vital signs for pain detection in critically ill TBI patients. Methods. Using a repeated measure within subject design, participants (N = 45) were observed for 1 minute before (baseline), during, and 15 minutes after two procedures: noninvasive blood pressure: NIBP (nonnociceptive) and turning (nociceptive). At each assessment, vital signs (e.g., systolic, diastolic, mean arterial pressure (MAP), heart rate (HR), respiratory rate (RR), capillary saturation (SpO2), end-tidal CO2, and intracranial pressure (ICP)) were recorded. Results. Significant fluctuations (P < 0.05) in diastolic (F = 6.087), HR (F = 3.566), SpO2 (F = 5.740), and ICP (F = 3.776) were found across assessments, but they were similar during both procedures. In contrast, RR was found to increase exclusively during turning (t = 3.933; P < 0.001) and was correlated to participants' self-report. Conclusions. Findings from this study support previous ones that vital signs are not specific for pain detection. While RR could be a potential pain indicator in critical care, further research is warranted to support its validity in TBI patients with different LOC.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".