Real-time evaluation of an automated computer vision system to monitor pain behavior in older adults
Bibliographic record
Abstract
Regular use of standardized observational tools to assess nonverbal pain behaviors results in improved pain care for older adults with severe dementia. While frequent monitoring of pain behaviors in long-term care (LTC) is constrained by resource limitations, computer vision technology has the potential to mitigate these challenges. A computerized algorithm designed to assess pain behavior in older adults with and without dementia was recently developed and validated using video recordings. This study was the first live, real-time evaluation of the algorithm incorporated in an automated system with community-dwelling older adults in a laboratory. Three safely-administered thermal pain tasks were completed while the system automatically processed facial activity. Receiver Operating Characteristic curves were used to determine the sensitivity and specificity of the system in identifying facial pain expressions using gold standard manual coding. The relationship between scoring methods was analyzed and gender differences were explored. Results supported the potential viability of the system for use with older adults. System performance improved when more intense facial pain expressiveness was considered. While average pain scores remained homogenous between genders, system performance was better for women. Findings will be used to further refine the system prior to future field testing in LTC.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".