Fluctuations in vital signs and behavioural responses of brain surgery patients in the Intensive Care Unit: are they valid indicators of pain?
Bibliographic record
Abstract
AIM: To examine the validity of behaviours and fluctuations in vital signs for pain assessment of postbrain surgery adults in the neurosurgical intensive care unit. BACKGROUND: Many patients in an intensive care unit may be unable to self-report their pain. In such cases, the use of observable indicators is recommended. Very little research has explored the validity of the use of behaviours and vital signs for pain assessment of neurocritically ill patients. DESIGN: Prospective repeated-measure within-subject observational design. METHODS: A total of 43 postbrain surgery patients were video recorded before, during and 15 minutes after a non-nociceptive (non-invasive blood pressure cuff inflation) and a nociceptive (turning) procedures. Their behaviours and vital signs were collected with a pre-tested behavioural checklist and a data collection computer connected to the bedside monitor. The patients' self-report of pain was obtained whenever possible. Data were collected between June-December in 2011. RESULTS: A larger number of pain-related behaviours were exhibited by participants during the nociceptive procedure compared with the non-nociceptive procedure supporting discriminant validation. Among vital signs, only respiratory rate differed significantly between the two procedures. Regarding criterion validation, only behaviours were positively correlated with self-reports of pain. CONCLUSION: Behaviours were found valid indicators of pain in neurocritically ill patients after elective brain surgery. Fluctuations in vital signs may suggest the presence of pain, but their validity for such use is not supported. They should only be used in combination with other validated pain assessment methods.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".