M is For Performance Validity: The IOP-M Provides a Cost-Effective Measure of the Credibility of Memory Deficits during Neuropsychological Evaluations
Bibliographic record
Abstract
This study was designed to evaluate the classification accuracy of the Memory module for the Inventory of Problems (IOP-M) in a sample of real-world patients. Archival data were collected from a mixed clinical sample of 90 adults clinically referred for neuropsychological testing. The classification accuracy of the IOP-M was computed against psychometrically defined invalid performance. IOP-M ≤30 produced a good combination of sensitivity (.46-.75) and specificity (.86-.95). Lowering the cutoff to ≤29 improved specificity (.94-1.00) at the expense of sensitivity (.29-.63). The IOP-M correctly classified between 73% and 91% of the sample. Given its low cost, ease of administration/scoring in combination with robust classification accuracy, the IOP-M has the potential to expand the existing toolkit for the evaluation of performance validity during neuropsychological assessments.
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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 | 0.021 | 0.039 |
| 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 | 0.002 |
| 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".