Malingered Neurocognitive Dysfunction in Neurotoxic Exposure: An Application of the Slick Criteria
Bibliographic record
Abstract
Toxic torts are increasing across the country and often the results of the neuropsychological evaluation are crucial for defining damages. Therefore, the accurate differentiation of those damaged by toxic exposure from those exaggerating or fabricating deficits is important. However, there is little research on malingering in this context. Presented are four patients claiming cognitive deficits after apparent occupational neurotoxic exposure who were diagnosed as malingering using the Slick, Sherman, and Iverson criteria. The goals of this article were to (1) illustrate the application of the Slick Criteria; (2) discuss current knowledge about the neurological and neurocognitive effects of toxic substances and its impact on clinical decision-making; (3) discuss the application of the Slick Criteria, specifically, and malingering research, generally, to toxic exposure cases; and (4) propose a paradigm in which medical, toxicological and neuropsychology professionals coordinately evaluate cases of alleged neurotoxic chemical exposure.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".