Bibliographic record
Abstract
Clinicians tend to assign greater weight to nonverbal expression than to patient self-report when judging the location and severity of pain. However, patients can be successful at dissimulating facial expressions of pain, as posed expressions resemble genuine expressions in the frequency and intensity of pain-related facial actions. The present research examined individual differences in the ability to discriminate genuine and deceptive facial pain displays and whether different models of training in cues to deception would improve detection skills. Judges (60 male, 60 female) were randomly assigned to 1 of 4 experimental groups: 1) control; 2) corrective feedback; 3) deception training; and 4) deception training plus feedback. Judges were shown 4 videotaped facial expressions for each chronic pain patient: neutral expressions, genuine pain instigated by physiotherapy range of motion assessment, masked pain, and faked pain. For each condition, the participants rated pain intensity and unpleasantness, decided which category each of the 4 video clips represented, and described cues they used to arrive at decisions. There were significant individual differences in accuracy, with females more accurate than males, but accuracy was unrelated to past pain experience, empathy, or the number or type of facial cues used. Immediate corrective feedback led to significant improvements in participants' detection accuracy, whereas there was no support for the use of an information-based training program.
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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 | 0.011 | 0.004 |
| 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 | 0.001 |
| 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".