Validation Testing of the European Portuguese Critical-Care Pain Observation Tool
Bibliographic record
Abstract
Aim. The study aim was to validate the Portuguese version of the Critical-Care Pain Observation Tool (CPOT) in the critically ill adult population of Portugal. Methods. A prospective, observational cohort study was conducted to evaluate the CPOT in mechanically ventilated patients who were admitted to an intensive care unit. A consecutive sample of 110 patients was observed at rest pre-procedure, during a nociceptive procedure (NP) which includes turning/positioning and endotracheal or tracheal suctioning and 20 min post-procedure. Two raters participated in the data collection. The discriminative validity, criterion validity, convergent validity and inter-rater reliability of the CPOT were examined. Results. The inter-rater reliability was excellent (0.93 ≤ α ≤ 1.00) at rest and fair to moderate (0.39 ≤ α ≤ 0.60) during the NP. The CPOT could discriminate between conditions with higher scores during the NP when compared to CPOT scores at rest (p < 0.001). The optimal CPOT cut-off score was >2, with a sensitivity of 71% and a specificity of 80%, and self-reported pain was the gold standard criterion. Significant correlations (<0.40) were found between CPOT scores, the heart rate and the respiratory rate during the nociceptive procedure. Conclusions. The CPOT appears to be a valid alternative for both ventilated and non-ventilated patients who are unable to communicate.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".