High Specificity of the Word Memory Test and Medical Symptom Validity Test in Groups with Severe Verbal Memory Impairment
Bibliographic record
Abstract
By definition, false positives occur when an otherwise very easy symptom validity test (SVT) or effort test is failed because of cognitive impairment and not because of poor effort. Therefore, the highest rate of false positives will be found in those groups with the most severe cognitive impairment. For that reason, it is important to study people with severe impairment when evaluating the specificity of SVTs. Some people with various types of dementia, notably those with Alzheimer's disease, suffer from severe impairment of memory and other cognitive abilities. In this study, patients with possible or probable dementia were tested with the Word Memory Test (WMT; Green, 2003; Green & Astner, 1995) and the Medical Symptom Validity Test (MSVT; Green, 2004). While some dementia patients failed the easy subtests of these instruments and had severe verbal memory impairment, they showed distinctive profiles of scores that have been reported to be characteristic of people with severe impairment. Using profile analysis, the WMT and MSVT achieved a specificity of 98.4% or higher in the patients of the current study. This suggests that there will be extremely low false positive rates using the same methods in people with relatively minor impairment of the type found in, for example, mild traumatic brain injury or depression.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".