Empirical Limits for the Forensic Assessment of PTSD Litigants.
Bibliographic record
Abstract
This paper discusses the limits of expert opinion on posttraumatic stress disorder (PTSD) in personal injury claims. The construct of PTSD is hampered by several empirical limitations. Multiple reliable measures of PTSD exist, but have not been evaluated sufficiently within litigating samples and are infrequently used by forensic assessors. Common methods for trauma screening appear insensitive. Opinions about causation of PTSD and disability are complicated by retrospective memory biases, as well as the failure of most anxiety disorders to be detected within primary medical care. PTSD appears to have a steep spontaneous remission curve during the first year, but at least 10% of trauma-exposed people suffer chronic distress. Little is known about the course beyond 1 year. Efficacious psychological treatments have been developed for PTSD, but are not in common use limiting claimants' access to rehabilitative treatments. Research on functional disability associated with PTSD is in its infancy, but it seems likely that PTSD will account for only a part of the variance in work disability. We provide suggestions for improving forensic practice, advising the courts about the limitations of forensic opinions, and necessary research.
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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 | 0.065 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".