Reliability and validity of observer ratings of pain using the visual analog scale (VAS) in infants undergoing immunization injections
Bibliographic record
Abstract
We tested the reliability and validity of observer-rated pain in infants undergoing immunization using the visual analog scale (VAS). Pain was assessed in real time and later, from videotapes, in 120 1-year-old infants participating in a double-blind randomized controlled trial of amethocaine vs. placebo. Altogether, 2 (1 physician, 1 non-physician) of 4 raters [2 physicians, 2 non-physicians (nurse and graduate student)] independently assessed baseline and vaccine injection pain using a 100mm unmarked VAS line. Intra- and inter-rater reliability, assessed using the intra-class correlation coefficient (ICC), ranged from 0.69 to 0.91 and 0.55 to 0.97, respectively. Bland-Altman plots demonstrated no evidence of bias between real time and video assessments. When scores were dichotomized into 2 groups (no pain and pain) using a cut-off of >or=30mm, intra-rater reliability ranged from 0.35 to 0.92. The percent of scores deviating by >20mm was 4.5-14.29%. Criterion validity was demonstrated by correlations between the VAS and Modified Behavioural Pain Scale, a validated observational measure; (rho: 0.81-0.94). Injection scores were lower in the amethocaine group, when comparing difference (baseline-injection) or dichotomized scores; significance (p<0.036) was achieved for non-physician scores, but not physician scores. Together, these results provide initial support for the VAS as an outcome measure for acute procedural pain in infants. However, different conclusions may be reached about the effectiveness of analgesic interventions depending on the rater. Sources of variability include use of multiple raters, rater focus (procedure vs. child) and experience level.
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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 | 0.023 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".