Computer-Animated Faces Pain Scale: Commentary on Fanciullo et al. (2007)
Bibliographic record
Abstract
The article by Fanciullo et al. [1], entitled “Development of a new computer method to assess children's pain,” raises many important issues regarding measurement and implementation problems associated with various self-report faces scales designed to assess pain in children. The authors introduce a computer-based faces scale that enables children to rate their pain on a continuous scale by adjusting the facial expression (shape of mouth and eyes) to indicate their pain intensity. We agree with the authors' statements concerning the potential advantages of a computer-based, continuous faces scale, namely sensitivity, patient preference, and computer data acquisition and display. Our experience with such a scale developed in 1997 [2–4] largely supports these assertions. The authors emphasized a presumed direct correspondence between pain intensity and the amount of curvature of the mouth and diameter of the eyes. However, in doing so, they seem to have ignored a basic rule of psychophysics. They assumed that physically equal changes (intervals) in facial expression are perceptually equal intervals. If that were the case, then Mona Lisa's smile would be nothing special. Subtle changes in …
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 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".