Validation of The Critical-care Pain Observation Tool (CPOT) for the detection of oral-pharyngeal pain in critically ill adults
Bibliographic record
Abstract
OBJECTIVE: Mechanically ventilated patients experience pain at rest and during daily care procedures. Our objective was to test the reliability and validity of the Critical-Care Pain Observation Tool (CPOT) to detect oral-pharyngeal pain in intubated and tracheostomised adults during routine oral care procedures. MATERIALS AND METHODS: Two trained research team members independently observed patients during two non-painful (rest and gentle touch) and three potentially painful (oral suctioning, tooth brushing, and swabbing with a sponge toothette) procedures. Conscious patients were asked if they experienced pain during each procedure (yes/no) and to rate their pain intensity on a 0 to 10 numeric rating scale. RESULTS: A total of 98 patients, primarily intubated (92.9%) and male (63.3%) participated. Criterion validation was supported by patient self-report of pain during tooth brushing (AUC=.80; P<0.5) and oral suction (AUC=.72; P<0.3) but not for oral swabbing (AUC=.68; P=0.16). Discriminative validation was demonstrated for all oral care procedures compared to rest (P<.001). Intra-class correlation coefficients between raters ranged from .78 to .91 (P<.001) for total CPOT scores, indicating excellent inter-rater reliability. CONCLUSIONS: The CPOT is reliable and valid for the detection of oral-pharyngeal pain during oral care procedures indicated as painful by critically ill adults.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".