The Assessment of Performance and Self-report Validity in Persons Claiming Pain-related Disability
Bibliographic record
Abstract
One third of all people will experience spinal pain in their lifetime and half of these will experience chronic pain. Pain often occurs in the context of a legally compensable event with back pain being the most common reason for filing a Workers Compensation claim in the United States. When financial incentives to appear disabled exist, malingered pain-related disability is a potential problem. Malingering may take the form of exaggerated physical, emotional, or cognitive symptoms and/or under-performance on measures of cognitive and physical capacity. Essential to the accurate detection of Malingered Pain-related Disability is the understanding that malingering is an act of will, the goal of which is to increase the appearance of disability beyond that which would naturally arise from the injury in question. This paper will review a number of Symptom Validity Tests (SVTs) that have been developed to detect malingering in patients claiming pain-related disability and will conclude with a review of studies showing the diagnostic benefit of combining SVT findings from a comprehensive malingering assessment. The utilization of a variety of tools sensitive to the multiple manifestations of malingering increases the odds of detecting invalid claims while reducing the risk of rejecting a valid claim.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".