Methods of Detecting Malingering and Estimated Symptom Exaggeration Base Rates in Australia
Bibliographic record
Abstract
Abstract Neuropsychology malingering base rates have not been widely investigated in Australia. Estimates in North America vary with as many as 4 in 10 people evaluated for personal injury or compensation cases suspected of exaggerating symptoms. Data on Australian neuro-psychology symptom exaggeration base rates were estimated using a modified and expanded version of a survey previously designed for this purpose (Mittenberg, Patton, Canyock, & Condit, 2002). Figures were based on an estimated 1818 annual cases involved in personal injury (n = 542), disability (n = 109), criminal (n = 108), or medical (n = 1059) matters. Symptom exaggeration base rates associated with referral type and diagnoses were variable. Specifically, 17% of criminal, 13% of personal injury, 13% of disability or workers compensation, and 4% of medical or psychiatric cases were reported to involve symptom exaggeration or probable symptom exaggeration. The highest rates of symptom exaggeration included cases referred for mild head injury (23%), pain or somatoform disorders (15%), moderate to severe head injury (15%), and fibromyalgia or chronic fatigue (15%). Overall, Australian symptom exaggeration base rates reported in this study were lower compared with base rates previously reported in North America.
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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 | 0.001 | 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 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".