Real-time assessment of pain behavior during clinical assessment of low back pain patients
Bibliographic record
Abstract
The development of procedures for assessing factors that contribute to pain and disability is crucial for clinical and epidemiologic studies. The present paper describes a system for in vivo, real-time assessment of pain behaviors integrated with a standardized physical examination for low back pain patients. The principles for measuring five categories of pain behavior--guarding, touching/rubbing, words, sounds and facial expressions--and for parsing the physical examination are described. The system was learned by five observers who then applied it during the physical examinations of 176 patients classified as suffering from sub-acute or chronic pain. The system was also applied to 77 patients in a test-retest consistency study. Measures of guarding, words, sounds and facial expression showed adequate psychometric properties. The test-retest consistency of guarding, sounds and facial expression was moderate-to-good, suggesting that these behaviors were consistent over the test-retest interval and promising for future study. The advantages and limitations of the technique are discussed and ways of modifying it to simplify coding and enhance the accuracy and reliability of its application are suggested. Overall, the technique shows promise for clinical and epidemiologic research.
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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 | 0.017 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".