Performance validity of the Dot Counting Test in a dementia clinic setting
Bibliographic record
Abstract
OBJECTIVE: This study examined the utility of a performance validity test (PVT), the Dot Counting Test (DCT), in individuals undergoing neuropsychological evaluations for dementia. We investigated specificity rates of the DCT Effort Index score (E-Score) and various individual DCT scores (based on completion time/errors) to further establish appropriate cutoff scores. METHOD: This cross-sectional study included 56 non-litigating, validly performing older adults with no/minimal, mild, or major cognitive impairment. Cutoffs associated with ≥90% specificity were established for 7 DCT scoring methods across impairment severity subgroups. RESULTS: Performance on 5 of 7 DCT scoring methods significantly differed based on impairment severity. Overall, more severely impaired participants had significantly higher E-Scores and longer completion times but demonstrated comparable errors to their less impaired counterparts. Contrary to the previously established E-Score cutoff of ≥17, a cutoff of ≥22 was required to maintain adequate specificity in our total sample, with significantly higher adjustments required in the Mild and Major Neurocognitive Disorder subgroups (≥27 and ≥40, respectively). A cutoff of >3 errors achieved adequate specificity in our sample, suggesting that error scores may produce lower false positive rates than E-Scores and completion time scores, both of which overemphasize speed and could inadvertently penalize more severely impaired individuals. CONCLUSIONS: In a dementia clinic setting, error scores on the DCT may have greater utility in detecting non-credible performance than E-Scores and completion time scores, particularly among more severely impaired individuals. Future research should establish and cross-validate the sensitivity and specificity of the DCT for assessing performance validity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".