Fifteen years later: Enhancing the classification accuracy of the performance validity module of the Advanced Clinical Solutions
Bibliographic record
Abstract
Objective The study was designed to evaluate the performance validity module of Advanced Clinical Solutions (ACS) against external criterion measures and compare two alternative aggregation methods for its five components.Method The ACS was evaluated against psychometrically defined criterion groups in a sample of 93 outpatients with TBI. In addition to the default method, the component performance validity tests (PVTs) were either dichotomized along a single cutoff (VI-ACS) or recoded to capture various degrees of failure (EI-ACS).Results The standard ACS model correctly classified 75–83% of the sample. The alternative aggregation methods produced superior overall correct classification: 80–91% (VI-ACS) and 86–91% (EI-ACS). Mild TBI was associated with higher failure rates than moderate/severe TBI. Failing just one of the five ACS components resulted in a 3- to 8-fold increase in the likelihood of failing criterion PVTs.Conclusions Results support the use of the standard PVT module for ACS: it is an effective measure of performance validity that is robust to moderate-to-severe TBI. Post-publication research on individual ACS components and methodological advances in PVT research provide an opportunity to enhance the overall classification accuracy of the ACS model. Passing stringent multivariate PVT cutoffs does not indicate valid performance.
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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 | 0.003 |
| 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 | 0.001 |
| 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".