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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".