Using facial expressions to assess musculoskeletal pain in older persons
Bibliographic record
Abstract
Past research examined measures of pain among seniors who were experiencing movement-related exacerbations of musculoskeletal pain and obtained clear support for the utility of the behavioural coding of pain-related body movements (e.g., bracing, guarding). Support for the utility of the Facial Action Coding System (FACS), which involves the objective coding of facial reactions, was not as strong. The findings concerning FACS could have been an artifact of the methodology that was used. Specifically, the duration of the facial reactions was not taken into account and the patients suffered from a variety of painful conditions. Thus, the physical activities involved in the study could have been painful for some patients but not for others. The present study corrected these methodological concerns by accounting for the duration of facial reactions and ensuring that all patients suffered from the same painful condition. Participants were 82 post-surgical (knee replacement) inpatients. Cognitive status was assessed using the Modified Mini Mental Status Examination. Under physiotherapist's supervision, the patients performed structured activities (i.e., reclining, standing, knee bends). Facial reactions were coded using FACS. Facial reactions varied as a function of the degree to which the various activities were strenuous. The results support the utility of FACS in the assessment of musculoskeletal pain among seniors undergoing rehabilitation following knee surgery.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".