Measures of Symptom Exaggeration for Mental Health Disorders: A Systematic Review
Bibliographic record
Abstract
Introduction .- Measures that help detect exaggeration of symptoms can be valuable for informing more accurate diagnoses and aid in treatment and case management. We completed a systematic review to identify measures that assess symptom exaggeration in mental health disorders. Methods .- Eligible studies assessed exaggeration of symptoms with a psychometrically validated measure in patients presenting with a mental health disorder. We searched MEDLINE and PsycINFO from inception to June 2013 for relevant studies. To determine study eligibility, reviewers screened title and abstracts of identified citations, and reviewed full texts of all potentially eligible citations. Data extractors completed data abstraction of eligible studies. Results .- Of 8435 unique citations, 105 studies consisting of 112 cohorts were eligible, and we identified 36 unique, validated measures assessing exaggeration of symptoms. The most frequently used measures were symptom validity indicators embedded in the Minnesota Multiphasic Personality Inventory (MMPI-2) (n=48, 46%), the Structured Interview of Reported Symptoms (SIRS) (n=12, 11%), and the Personality Assessment Inventory (PAI) (n=11, 10%). Most studies (n=96; 91%) failed to test reliability of their measure of symptom exaggeration. The symptom validity indicators in the MMPI/MMPI-2 and the SIRS both showed moderate to high internal consistency, range 0.47 to 0.85 and 0.48 to 0.95, respectively. Conclusions .- Multiple measures assessing symptom exaggeration have been used in patients with mental health disorders. The symptom validity indicators of the MMPI/MMPI-2 are the most widely used measures to assess symptom exaggeration. Assessment and reporting of reliability is poor across studies; we require further assessment of psychometric properties for existing measures of symptom exaggeration.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".