Eye movement desensitization and reprocessing for post-stroke post-traumatic stress disorder: Case report using the three-phase approach
Bibliographic record
Abstract
Medically-induced post-traumatic stress disorder (PTSD) is substantially more prevalent than PTSD in the general population. In people with stroke, it can impact as many as 23% of patients, with negative effects on mental health as well as stroke-related disability. Medically-induced PTSD may have unique features compared to other forms of PTSD, and therefore there is a pressing need to evaluate existing treatments for PTSD in this context. The current study reports on the feasibility, safety, and efficacy of Eye Movement Desensitization and Reprocessing (EMDR) for PTSD subsequent to a pontine stroke. Using a quasi-experimental case design, a 44-year-old Caucasian woman received EMDR delivered via telehealth. Self-report measures were obtained at baseline, pre-EMDR, and post-EMDR, with brief neuropsychological testing pre/post-EMDR. After 3 sessions of EMDR, the patient no longer met criteria for PTSD, and showed clinically significant reductions in depressive and generalized anxiety symptoms. With proper safety provisions, it is feasible to deliver EMDR via telehealth to alleviate post-stroke PTSD. Reduced linguistic demands of EMDR may be particularly appealing for persons with neurological disorders as compared to other trauma therapies. Further work is also needed to understand the parameters of baseline neuropsychological function that could impact response to intervention.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".