Validation of the Critical-Care Pain Observation Tool (CPOT) in pediatric patients undergoing orthopedic surgery
Bibliographic record
Abstract
Background: Postoperative pain cannot be measured accurately among many children with intellectual and developmental disabilities, resulting in underrecognition or delay in recognition of pain. The Critical-Care Pain Observation Tool (CPOT) is a pain assessment tool that has been widely validated in critically ill and postoperative adults. Aims: The objective of this study was to validate the CPOT for use with pediatric patients able to self-report and undergoing posterior spinal fusion surgery. Methods: Twenty-four patients (10-18 years old) scheduled to undergo surgery were consented to this repeated-measure, within-subject study. To examine discriminative and criterion validation, CPOT scores and patients' self-reports of pain intensity were collected prospectively by a bedside rater before, during, and after a nonnociceptive and nociceptive procedure on the day following surgery. Patients' behavioral reactions were video recorded at the bedside and retrospectively viewed by two independent video raters to examine interrater and intrarater reliability of CPOT scores. Results: Discriminative validation was supported with higher CPOT scores during the nociceptive procedure than during the nonnociceptive procedure. Criterion validation was supported with a moderate positive correlation between the CPOT scores and the patients' self-reported pain intensity during the nociceptive procedure. A CPOT cutoff score of ≥2 was associated with the maximum sensitivity (61.3%) and specificity (94.1%). Reliability analyses revealed poor to moderate agreement between bedside and video raters and moderate to excellent consistency within video raters. Conclusions: These findings suggest that the CPOT may be a valid tool to detect pain in pediatric patients in the acute postoperative inpatient care unit after posterior spinal fusion.
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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 | 0.007 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".