Meta-analytic Re-assessment of the Validity of Miller Forensic Assessment Test for Detection of Malingering
Bibliographic record
Abstract
Background: The Miller Forensic Assessment of Symptoms Test (M-FAST) is used widely for the assessment of malingering of medical symptoms. Its validity has allegedly been supported by meta-analytic study of M-FAST in 2019 by Detullio et al. Credibility of Detullio’s results is damaged by an inclusion of data based on analog validation and also on dubious convergent validation procedures that falsify estimates of M-FAST’s validity. Method: In the present study, the meta-analysis was calculated on 3 types of M-FAST data: (1) 5 samples of scores of healthy persons instructed to respond honestly, (2) 5 samples of scores of medical patients, and (3) 10 samples of scores of healthy persons instructed to feign mental illness (so called “instructed malingerers”). Results: In an ANOVA (F(2,815)=398.50, p<.0001), significantly lowest M-FAST scores were those of healthy controls (mean=1.59, SD=2.80), the next significantly higher scores were those of legitimate patients (mean=4.85, SD=4.22), and the instructed malingerers had significantly highest scores (mean=12.34, SD=5.71). Discussion: The significant difference between healthy controls and patients shows that inferences from analog validations of the M-FAST are inherently false. Furthermore, data of legitimate patients with severe psychiatric illness suggested that they may face the risk of about 50% to be falsely classified as malingerers by the M-FAST. Moreover, almost all validations of the M-FAST were done only with “instructed malingerers” (healthy volunteers instructed to feign symptoms). This overestimates the test’s capacity to detect real-life malingerers. Montes and Guyton documented that “instructed malingerers” warned to avoid detection score much lower than the unwarned ones (effect size: Cohen’s d=3.05). M-FAST’s capacity for detection of real-life malingerers may be extremely low, in particular those more genuinely motivated to evade detection, well prepared, better educated, and systematically feigning only a few specific symptoms such as depression, pain, and insomnia. Conclusion: The M-FAST should no longer be used.
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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 | 0.100 | 0.195 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.038 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".