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)
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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".