Facial Expression Overrides Lumbopelvic Kinematics for Clinical Judgements about Low Back Pain Intensity
Bibliographic record
Abstract
Background. Through real-time behavioral observation systems, pain behaviors are commonly used by clinicians to estimate pain intensity in patients with low back pain. However, little is known about how clinicians rely on pain-related behaviors to make their judgment. According to the Information Integration Theory (IIT) framework, this study aimed at investigating how clinicians value and integrate information from lumbopelvic kinematics (LK), a protective pain behavior, and facial expression intensity (FEI), a communicative pain behavior, to estimate pain in patients with chronic low back pain (cLBP). Methods. Twenty-one experienced clinicians and twenty-one novice clinicians were asked to estimate back pain intensity from a virtual character performing a trunk flexion-extension task. Results. Results revealed that both populations relied on facial expression and that only half of the participants in each group integrated FEI and LK to estimate cLBP intensity. Among participants who integrated the two pain behaviors, averaging rule predominated among others. Results showed that experienced clinicians relied equally on FEI and LK to estimate pain, whereas novice clinicians mostly relied on FEI. Discussion. The use of additive rule of integration does not appear to be systematic when assessing others' pain. When assessing pain intensity, communicative and protective pain behaviors may have different relevance.
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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 | 0.004 | 0.039 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".