Deceptive Clinical Diagnosing of Malingering via Structured Inventory of Malingered Symptomatology
Bibliographic record
Abstract
This article provides illustrative case histories of patients with legitimate neuropsychological symptoms after their motor vehicle accidents (MVAs) who had been rejected as malingerers by the psychologist contracted by the car insurance company. The psychologist ignored the physical facts of the MVA (such as repeated major impacts) to instead blindly rely on the patient's scores on the Structured Inventory of Malingered Symptomatology (SIMS). The SIMS was never properly validated on patients with neuropsychological symptoms such as the post-concussion syndrome or on patients with well documented causes of chronic pain: the test has scientifically very inept rates of false positives, i.e., of patients with legitimate symptoms falsely classified as malingerers. The SIMS contains many items inquiring about impaired sleep, depressive feelings, impaired memory or concentration, and other typical post-MVA neuropsychological symptoms such as tinnitus or impaired balance. That is, these items describe what is clinically known to be legitimate typical post-MVA symptoms: in an absurd manner, the endorsement of these SIMS items counts as "malingering" and alone causes the post-MVA patients to accumulate a score above the SIMS cut-off point of > 14, thus misclassifying them as malingerers. The more of these symptoms are experienced by the patient, the more likely is he or she to be classified as a malingerer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".