Interrater Agreement between Bedside and Video Raters Using the CPOT-Neuro for Pain Assessment in Critically Ill Patients with a Brain Injury
Bibliographic record
Abstract
This study aimed to examine the interrater agreement of Critical-Care Pain Observation Tool-Neuro (CPOT-Neuro) scores as a newly developed tool for pain assessment in patients with critical illness and brain injury between raters using two methods of rating (bedside versus video) during standard care procedures (i.e., non-invasive blood pressure and turning). The bedside raters were research staff, and the two video raters had different backgrounds (health and non-health disciplines). Raters received standardized 45 min training by the principal investigator. Video recordings of 56 patient participants with a brain injury at different levels of consciousness were included. Interrater agreement was supported with an Intraclass Correlation Coefficient (ICC) > 0.65 for all pairs of raters and for each procedure. Interrater agreement was highest during turning in the conscious group, with ICCs ranging from 0.79 to 0.90. The use of video recordings was challenging for the observation of some behaviors (i.e., tearing, face flushing), which were influenced by factors such as lighting and the angle of the camera. Ventilator alarms were also challenging to distinguish from other sources for the video rater from a non-health discipline. Following standardized training, video technology was useful in achieving an acceptable interrater agreement of CPOT-Neuro scores between bedside and video raters for research purposes.
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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 | 0.035 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".