The stigmatization of patients with chronic pain due to assessed exaggeration of symptoms
Bibliographic record
Abstract
Patients with chronic pain that cannot be explained by tissue abnormality may be accused of symptom amplification and at worst malingering. This is particularly relevant in the medicolegal setting where legal decisions are highly dependent on objective and validated information, conditions mostly lacking in the setting of chronic pain. When evaluations are conducted by assessors less familiar with current knowledge of pain mechanisms, subjective complaints of pain and associated symptoms such as fatigue and cognitive difficulties, are at risk of being misinterpreted leading to bias and stigmatization. In this commentary we will highlight some of the pitfalls that erroneously lead to a biased assessment of pain severity including failure to pay attention to psychological state and sociocultural influences, application of poorly reliable physical maneuvers, and use of neurocognitive testing of intentional cognitive dysfunction as a surrogate for dishonesty in pain and functional impairment report. Concerns about misinterpretation of exaggeration in persons with chronic pain are highlighted by recent report of symptom exaggeration in up to two thirds of those attending for an independent medical evaluation. Directives to help the medical assessor to provide pertinent information that will assist the courts in reaching a fair decision are discussed, with emphasis on need for a comprehensive assessment of biosocial factors, contextual variables and nonphysical evidence.
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| 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".