Meta-Analysis of SIMS Scores of Survivors of Car Accidents and of Instructed Malingerers
Bibliographic record
Abstract
Objective: To compare scores on the Structured Inventory of Malingered Symptomatology (SIMS) of normal controls, survivors of motor vehicle accidents (MVAs), and of malingerers instructed to feign post-MVA symptoms.Method: Mean score and SD was calculated by combining published data on 9 samples of normal controls (combined N=500).Similarly, mean score and SD was calculated by combining published data of 4 samples of persons instructed to feign post-MVA symptoms (combined N=88).Then, ANOVAs were calculated to compare SIMS data of 4 groups: (1) the combined sample of 500 normal controls, (2) 47 patients with minor injuries from MVAs (data published by Capilla Ramírez et al. in 2014), (3) 23 patients injured in high impact MVAs (data published in Cernovsky et al. in 2019), and (4) the combined sample of 88 instructed malingerers.Results: The ANOVAs were calculated separately for the SIMS total score and then also separately for each of the 5 SIMS scales.The results of these ANOVAs were all significant and, with a few exceptions, post-hoc tests followed the following pattern: (1) the controls obtained significantly lower scores than either of the two groups of patients and also than the instructed malingerers, (2) patients with minor injuries scored lower than those injured in high impact MVAs and also lower than instructed malingerers, (3) patients injured in high impact MVAs had SIMS scores similar to persons instructed to feign post-MVA symptoms (with some exceptions). Discussion and Conclusions:The overall meta-analytic pattern indicates that patients injured in high impact MVAs and persons instructed to feign post-MVA symptoms tend to obtain similar SIMS scores (with some exceptions) and that both groups score higher than normal controls.This is consistent with the previously published findings that the SIMS consists only of items describing legitimate medical symptoms (SIMS scales NI, AM, AF, P) and of arithmetic and logical tasks and items assessing general knowledge (SIMS LI scale).The SIMS is a pseudoscientific test that fails to differentiate legitimate medical patients from malingerers.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.034 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".