An Australian study on feigned mTBI using the Inventory of Problems – 29 (IOP-29), its Memory Module (IOP-M), and the Rey Fifteen Item Test (FIT)
Why this work is in the frame
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Bibliographic record
Abstract
We investigated the classification accuracy of the Inventory of Problems − 29 (IOP-29), its newly developed memory module (IOP-M) and the Fifteen Item Test (FIT) in an Australian community sample (N = 275). One third of the participants (n = 93) were asked to respond honestly, two thirds were instructed to feign mild TBI. Half of the feigners (n = 90) were coached to avoid detection by not exaggerating, half were not (n = 92). All measures successfully discriminated between honest responders and feigners, with large effect sizes (d ≥ 1.96). The effect size for the IOP-29 (d ≥ 4.90), however, was about two-to-three times larger than those produced by the IOP-M and FIT. Also noteworthy, the IOP-29 and IOP-M showed excellent sensitivity (>90% the former, > 80% the latter), in both the coached and uncoached feigning conditions, at perfect specificity. Instead, the sensitivity of the FIT was 71.7% within the uncoached simulator group and 53.3% within the coached simulator group, at a nearly perfect specificity of 98.9%. These findings suggest that the validity of the IOP-29 and IOP-M should generalize to Australian examinees and that the IOP-29 and IOP-M likely outperform the FIT in the detection of feigned mTBI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it