High Specificity of the Medical Symptom Validity Test in Patients with Very Severe Memory Impairment
Bibliographic record
Abstract
Failure on effort tests usually implies insufficient effort to produce valid cognitive test scores. However, many people with very severe cognitive impairment, such as dementia patients, will produce failing scores on nearly all effort tests. In such patients, effort tests have low specificity. The Medical Symptom Validity Test (MSVT) and the nonverbal MSVT (NV-MSVT) were designed to address this problem. They produce profiles of scores across multiple subtests to facilitate discrimination between low scores from people trying to feign impairment and low scores attributable to severe impairment. To study the specificity of the MSVT and NV-MSVT in people with very severe memory impairment, we tested (a) 10 institutionalized patients with dementia and (b) 10 volunteers who were asked to simulate memory impairment. It was hypothesized that the "possible dementia profile" would be found significantly more often in the dementia patients than in the simulators. The MSVT and the NV-MSVT both displayed 100% specificity in the dementia group, while retaining a combined sensitivity of 80% to suboptimal effort in the simulator group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".