Evaluation of Digital Face Recognition Technology for Pain Assessment in Young Children
Bibliographic record
Abstract
OBJECTIVES: Accurate assessment of pain in young children is challenging. An Emotion Application Programing Interface (API) can analyze and report 8 emotions from facial images. Each emotion ranges between 0 (no correlation) to 1 (greatest correlation). We evaluated correlation between the Emotion API with the FLACC scale (face, lets, activity, cry, and consolability) among children younger than 6 years old during blood sampling. METHODS: Prospective pilot exploratory study in children during blood sampling. Pictures with facial expressions were uploaded to Emotion API program. Primary outcome was the correlation coefficient between FLACC scale and emotions. Secondary outcomes included maximal correlation of each emotion for 3 pictures-before, during and after needle penetration; and the average of each emotion for 9 pictures-4 before, 1 during and 4 after needle penetration to the skin. RESULTS: A total of 77 children were included. During needle penetration, SADNESS was significantly correlated (0.887, P<0.05), and NEUTRAL was negative correlated with the FLACC scale (-0.841; P<0.05). The maximal correlation of each emotion showed increase in SADNESS and decrease in NEUTRAL emotions during, compared to before, needle penetration. Similar findings were observed when the average of each emotion was compared during to before needle penetration. DISCUSSION: During a blood test procedure, young children show higher SADNESS and lower NEUTRAL emotions as reported by the Emotion API. This software program may be useful in reporting emotions related to pain in young children, and more research is needed to compare its validity, reliability and real-time application compared to the FLACC scale.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".