Symptom Profile of Injured Motorists on the Structured Inventory of Malingered Symptomatology
Bibliographic record
Abstract
Background: Recent extensive content analyses demonstrated that the items of the Structured Inventory of Malingered Symptomatology (SIMS) have no capacity to differentiate malingerers from legitimate medical patients: all items list or assess legitimate medical symptoms. We examined which SIMS items are the most frequently endorsed by injured motorists. Method: De-identified archival data of 23 survivors (mean age=38.0, SD=12.8) of high impact motor vehicle accidents (MVAs) contained their SIMS scores, their responses to the Brief Pain Inventory, Morin’s Insomnia Severity Index, Rivermead Post-Concussion Symptoms Questionnaire, and to the Post-MVA Neurological Symptoms scale. Results: All SIMS items which were endorsed by more than 43% of the patients in the directions scored by the SIMS as indicative of “malingering” were selected. Twenty-five items met this criterion. On a closer examination, all these 25 items are legitimate psychological and neuropsychological symptoms typically experienced by injured motorists, such as depression, impaired sleep, and postconcussive symptoms (memory and concentration problems, impaired balance) and whiplash symptoms (numbness in the limbs, instances of reduced muscular control over some of the limbs). Discussion and Conclusions: The 25 endorsed items are consistent with the polytraumatic symptom profile of injured patients. In a travesty of psychological assessment, these symptoms are scored in the SIMS as denoting “malingering”.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".