Faces of clinical pain: Inter‐individual facial activity patterns in shoulder pain patients
Bibliographic record
Abstract
BACKGROUND: Facial activity during pain is composed of varying combinations of a few elementary facial responses (so-called Action Units). A previous study of experimental pain showed that these varying combinations can be clustered into distinct facial activity patterns of pain. In the present study, we examined whether comparable facial activity patterns can also be identified among people suffering from clinical pain; namely, shoulder pain. METHODS: Facial expressions of patients suffering from shoulder pain (N = 126) were recorded while twice undergoing a battery of passive range-of-motion tests to their affected limbs (UNBC-McMaster Shoulder Pain Expression Archive Database), which elicited peaks of acute pain. Facial expressions were analysed using the Facial Action Coding System to extract facial Action Units (AUs). Hierarchical cluster analyses were used to look for characteristic combinations of these AUs. RESULTS: Cluster analyses revealed four distinct activity patterns during painful movements. Each cluster was composed of different combinations of pain-indicative AUs, with one AU common to all clusters, namely, "narrowed eyes". Besides these four clusters, there was a "stoic" pattern, characterized by no discernible facial action. The identified clusters were relatively stable across time and comparable to the facial activity patterns found previously for experimental heat pain. CONCLUSIONS: These findings corroborate the hypothesis that facial expressions of acute pain are not uniform. Instead, they are composed of different combinations of pain-indicative facial responses, with one omnipresent response, namely, "narrowed eyes". Raising awareness about these inter-individually different "faces of pain" could improve the recognition and, thereby, its diagnostic training for professionals, like nurses and physicians. SIGNIFICANCE: Similar to experimental pain, facial activity during evoked pain episodes in shoulder pain patients could be clustered into distinct faces of pain. Each cluster was composed of different combinations of single facial responses, namely: narrowed eyes, which is displayed either alone or in combination with opened mouth or wrinkled nose, or furrowed brows and closed eyes. These distinct faces of pain may inform the training of professionals and computers how to best recognize pain based on facial expressions.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".