High Risk of False Classification of Injured People as Malingerers by the Structured Inventory of Malingered Symptomatology (SIMS): A Review
Bibliographic record
Abstract
Background: Tests that purport to measure malingering such as the Structured Inventory of Malingered Symptomatology (SIMS) are associated with a risk to the public.The magnitude of this risk can be operationalized as the frequency of false positives, i.e., proportion of persons classified as malingerers and thus denied therapy and other medical benefits.Method: This review deals with the outcomes of studies of content, divergent, and criterion validity of the SIMS.We calculated an average risk to the public caused by the rates of false positives in published SIMS data on several clinical groups: psychiatric patients, survivors of high impact motor vehicle accidents (MVAs), and trauma-exposed war veterans. Results:(1) Content analyses demonstrated that almost all SIMS items describe medical symptoms, but these are fallaciously scored by the SIMS as indicative of malingering.(2) Calculations of divergent validity suggest that the SIMS measures the presence of medical symptoms rather than their malingering.For instance, SIMS total score correlates positively and highly with PCL-5 measure of PTSD (r=.60).The SIMS Amnestic Disorder scale correlates positively with Rivermead measure of post-concussive symptoms (r=.42).The SIMS Neurological Impairment scale correlates positively with neuropsychological symptoms measured by Post-MVA Neurological Symptoms (PMNS) scale (r=.41).(3) Criterion validity results of a recent meta-analysis indicated no significant capacity of the SIMS to differentiate legitimate patients from malingerers.Furthermore, published SIMS data indicate extremely high rates of false positives: 82.7% of US veterans with PTSD, 78.3% of patients injured in high impact MVAs, and 72.0% of legitimate psychiatric inpatients.An average risk to the public (i.e., the risk for genuine medical patients to be falsely classified as malingering) as suggested by the weighted mean for these SIMS data is 78.8%.Patients with more medical symptoms are significantly more likely to be fallaciously classified by their SIMS scores as "malingerers" than their less symptomatic counterparts. Discussion and Conclusions:The SIMS is a fatally flawed psychological test with alarmingly high iatrogenic rates.Its use constitutes malpractice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".