Estimating the accuracy of neurocognitive effort measures in the absence of a “gold standard”.
Bibliographic record
Abstract
Psychologists frequently use symptom validity tests (SVTs) to help determine whether evaluees' test performance or reported symptoms accurately represent their true functioning and capability. Most studies evaluating the accuracy of SVTs have used either known-group comparisons or simulation designs, but these approaches have well-known limitations (potential misclassifications or lack of ecological validity). This study uses latent class modeling (LCM) implemented in a Bayesian framework to estimate SVT classification accuracy based on data obtained from real-life forensic evaluations. We obtained archival data from 1,301 outpatient evaluees who underwent testing with the Computerized Assessment of Response Bias (CARB), the Test of Memory Malingering (TOMM), and the Word Memory Test (WMT) in a forensic evaluation context. Under various data models, Markov chain Monte Carlo methods implemented via WinBUGS converged to target distributions that permitted Bayesian estimates of SVT accuracy. Under the most plausible model (conditional dependence in test results), classification accuracies (expressed as area under the "trapezoidal" receiver operating characteristic curve ± standard deviation) were as follows: CARB = 0.765 ± 0.030, WMT = 0.929 ± 0.020, and TOMM = 0.771 ± 0.034. At decision thresholds that hold false positive rates at 0.02, the SVTs would detect invalid responses (true positives) at rates of approximately 35%, 65%, and 49%, respectively, for the 3 tests. Though LCM methods have limitations, this study suggests that they offer an approach to SVT evaluation that avoids methodological pitfalls of known-group research designs while retaining ecological validity that is absent in simulation studies.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".