Cross-Validating the Atypical Response Scale of the TSI-2 in a Sample of Motor Vehicle Collision Survivors
Bibliographic record
Abstract
Abstract This study was designed to evaluate the utility of the Atypical Responses (ATR) scale of the Trauma Symptom Inventory – Second Edition (TSI-2) as a symptom validity test (SVT) in a medicolegal sample. Archival data were collected from a consecutive case sequence of 99 patients referred for neuropsychological evaluation following a motor vehicle collision. The ATR’s classification accuracy was computed against criterion measures consisting of composite indices based on SVTs and performance validity tests (PVTs). An ATR cutoff of ≥ 9 emerged as the optimal cutoff, producing a good combination of sensitivity (.35-.53) and specificity (.92-.95) to the criterion SVT, correctly classifying 71–79% of the sample. Predictably, classification accuracy was lower against PVTs as criterion measures (.26-.37 sensitivity at .90-.93 specificity, correctly classifying 66–69% of the sample). The originally proposed ATR cutoff (≥ 15) was prohibitively conservative, resulting in a 90–95% false negative rate. In contrast, although the more liberal alternative (≥ 8) fell short of the specificity standard (.89), it was associated with notably higher sensitivity (.43-.68) and the highest overall classification accuracy (71–82% of the sample). Non-credible symptom report was a stronger confound on the posttraumatic stress scale of the TSI-2 than that of the Personality Assessment Inventory. The ATR demonstrated its clinical utility in identifying non-credible symptom report (and to a lesser extent, invalid performance) in a medicolegal setting, with ≥ 9 emerging as the optimal cutoff. The ATR demonstrated its potential to serve as a quick (potentially stand-alone) screener for the overall credibility of neuropsychological deficits. More research is needed in patients with different clinical characteristics assessed in different settings to establish the generalizability of the findings.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".