A New Set of Criteria for Evaluating Malingering in Work-Related Vestibular Injury
Bibliographic record
Abstract
OBJECTIVE: To develop and use a set of criteria using computerized dynamic posturography results to detect aphysiologic behavior in patients with complaints of imbalance, and to compare the efficacy of this system with present quantitative techniques and with subjective methods of evaluating malingerers. STUDY DESIGN: Prospective study of two groups of sequentially referred patients complaining of dizziness and/or imbalance. SETTING: A tertiary and quaternary care ambulatory referral center. PATIENTS: Two groups of patients were studied. One was a group of patients who had suffered work-related head trauma and had subsequent complaints of dizziness and/or imbalance. The other was a group of patients referred for dizziness and/or imbalance who had no history of head trauma, work-related injury, or litigation procedures. INTERVENTIONS: Standard vestibular assessment including computerized dynamic posturography was carried out on all patients. MAIN OUTCOME MEASURES: All patients in both groups were scored for aphysiologic behavior using a quantified formula that has been used to detect malingerers, an analysis criterion using different aspects of computerized dynamic posturography performance that has been used for some time, and our newly developed nine-point scoring method. Results of all three methods were compared to determine the effectiveness of each one in detecting malingering behavior. RESULTS: Our nine-point protocol was effective in assessing patients in a consistent manner. Assessment using the quantitative formula in the literature raised suspicions of malingering in many patients with no known ulterior motives, some of whom had documented vestibular disease. Our criteria also evaluate other aspects of performance not evaluated by the presently used techniques. CONCLUSION: Our newly developed criteria are effective at evaluating the medicolegal patients from both the quantitative and qualitative viewpoints, and provide a more thorough assessment than has previously been available.
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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 | 0.000 | 0.002 |
| 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".