Pain Assessment in Children
Bibliographic record
Abstract
OBJECTIVES: Assessing pain in young children requires astute judgment by observers. Multidimensional observational scales for pediatric pain contribute by providing behavioral cues believed to characterize pain in children; yet, few measurement items have undergone rigorous psychometric evaluation. This is the case with facial expression, which has been widely recognized as the most sensitive and specific nonverbal indicator of pain. The criteria for identifying facial expressions of pain differ substantially across scales and are frequently inconsistent with empirical descriptions. MATERIALS AND METHODS: The present study compared observer ratings of children's (aged 1 to 6 y, inclusive) videotaped postoperative pain reactions using the facial activity items from 6 widely used pediatric pain assessment scales and an anatomically based and empirically validated measure, the Child Facial Coding System. We hypothesized that facial expression items that did not correspond to empirical descriptions would lead to less reliable and divergent pain estimates. Intercoder reliability, criterion validity (empirical and convergent), content validity, and face validity were examined. RESULTS: Findings supported hypotheses and indicated that variation in cues proposed for assessing facial expression led to widely ranging scores that could be insensitive to differences in children's pain intensity. DISCUSSION: The facial items varied considerably in coder judgment reliability as well as criterion (empirical and convergent), content, and face validity. Observational scales should provide behavioral cues that correspond to empirical descriptions of the facial expression of pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".