The Mild Brain Injury Atypical Symptoms (mBIAS) scale in a mixed clinical sample
Bibliographic record
Abstract
INTRODUCTION: The Mild Brain Injury Atypical Symptoms (mBIAS) scale was developed as a symptom validity test (SVT) for use with patients following mild traumatic brain injury. This study was the first to examine the clinical utility of the mBIAS in a mixed clinical sample presenting to a Department of Veterans Affairs (VA) neuropsychology clinic. METHOD: Participants were 117 patients with mixed etiologies (85.5% male; age: M = 39.2 years, SD = 11.6) from a VA neuropsychology clinic. Participants were divided into pass/fail groups using two different SVT criteria, based on select validity scales from the Minnesota Multiphasic Personality Inventory-2 (MMPI-2): first, Infrequency Scale (F) scores: (a) MMPI-F-Fail (n = 21) and (b) MMPI-F-Pass (n = 96); and, second, Symptom Validity Scale (FBS) scores: (a) MMPI-FBS-Fail (n = 36) and (b) MMPI-FBS-Pass (n = 81). RESULTS: The mBIAS demonstrated good internal consistency, and each item contributed meaningfully to the total score. At a symptom exaggeration base rate of 35%, an mBIAS cutoff of ≥11 was optimal for screening symptom exaggeration when groups were classified using both F and FBS scales. This cutoff score resulted in very high specificity (.89 to .94); moderate-high positive predictive power (.71 to .75) and negative predictive power (.72 to .79); and low-moderate sensitivity (.31 to .57). At all base rates of probable somatic exaggeration, a cutoff of ≥16 resulted in perfect specificity and positive predictive power, but very low sensitivity. CONCLUSIONS: The mBIAS has potential for use in samples outside of mild traumatic brain injury. In settings where the symptom exaggeration base rate is 35%, a cutoff of ≥11 may be used as a "red flag" for further evaluation, but should not be relied on for clinical decision making. At all base rates of probable somatic exaggeration, psychologists with patients who score ≥16 can be confident that those patients were exaggerating. Importantly, however, this cutoff may fail to identify a large proportion of patients who are exaggerating.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".