They are not destined to fail: a systematic examination of scores on embedded performance validity indicators in patients with intellectual disability
Bibliographic record
Abstract
This study was designed to determine the clinical utility of embedded performance validity indicators (EVIs) in adults with intellectual disability (ID) during neuropsychological assessment. Based on previous research, unacceptably high (>16%) base rates of failure (BRFail) were predicted on EVIs using on the method of threshold, but not on EVIs based on alternative detection methods. A comprehensive battery of neuropsychological tests was administered to 23 adults with ID (MAge = 37.7 years, MFSIQ = 64.9). BRFail were computed at two levels of cut-offs for 32 EVIs. Patients produced very high BRFail on 22 EVIs (18.2%-100%), indicating unacceptable levels of false positive errors. However, on the remaining ten EVIs BRFail was <16%. Moreover, six of the EVIs had a zero BRFail, indicating perfect specificity. Consistent with previous research, individuals with ID failed the majority of EVIs at high BRFail. However, they produced BRFail similar to cognitively higher functioning patients on select EVIs based on recognition memory and unusual patterns of performance, suggesting that the high BRFail reported in the literature may reflect instrumentation artefacts. The implications of these findings for clinical and forensic assessment are discussed.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".