Impact of criterion measures on the classification accuracy of TOMM-1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study was designed to examine the effect of various criterion measures on the classification accuracy of Trial 1 of the Test of Memory Malingering (TOMM-1), a free-standing performance validity test (PVT). Archival data were collected from a case sequence of 91 (MAge = 42.2 years; MEducation = 12.7) patients clinically referred for neuropsychological assessment. Trials 2 and Retention of the TOMM, the Word Choice Test, and three validity composites were used as criterion PVTs. Classification accuracy varied systematically as a function of criterion PVT. TOMM-1 ≤ 43 emerged as the optimal cutoff, resulting in a wide range of sensitivity (.47–1.00), with perfect overall specificity. Failing the TOMM-1 was unrelated to age, education or gender, but was associated with elevated self-reported depression. Results support the utility of TOMM-1 as an independent, free-standing, single-trial PVT. Consistent with previous reports, the choice of criterion measure influences parameter estimates of the PVT being calibrated. The methodological implications of modality specificity to PVT research and clinical/forensic practice should be considered when evaluating cutoffs or interpreting scores in the failing range.
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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 | 0.000 | 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 it