Pain Assessment in the Critically Ill Ventilated Adult: Validation of the Critical-Care Pain Observation Tool and Physiologic Indicators
Bibliographic record
Abstract
OBJECTIVES: Use of a valid behavioral measure for pain is highly recommended for critically ill, uncommunicative adults. The aim of this study was to validate the English version of the Critical-Care Pain Observation Tool (CPOT) and physiologic indicators [mean arterial pressure, heart rate, respiratory rate, and transcutaneous oxygen saturation (SpO(2))] in critically ill ventilated adults. METHODS: A total of 30 conscious and 25 unconscious patients in the intensive care unit participated in the study. Patients were assessed by staff nurses and research team members before, during, and 20 minutes after the 2 following procedures: (1) nociceptive procedure: turning, and (2) non-nociceptive procedure: taking noninvasive blood pressure (NIBP). Conscious ventilated patients provided self-report level of pain. RESULTS: Interrater reliability of the CPOT was supported with high intraclass correlation coefficients (0.80 to 0.93). Discriminant validity was supported with increases of the CPOT and physiologic indicators, and a decrease in SpO(2) during turning, but remaining stable during NIBP. Conscious patients had higher CPOT scores during turning compared with unconscious patients. For criterion validity, the CPOT scores were correlated to the patients' self-reports of pain, whereas physiologic measures were not. Using a CPOT cutoff score of >3 yielded a sensitivity of 66.7% and a specificity of 83.3%. DISCUSSION: The CPOT is a reliable and valid tool to assess pain in critically ill adults. Behavioral indicators represent more valid information in pain assessment than physiologic indicators. Further research is needed to explore how specific critically ill populations (eg, head injury) react to a painful procedure.
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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 | 0.008 | 0.024 |
| 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 | 0.001 | 0.001 |
| 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".