81POST-TRAUMATIC STRESS DISORDER AFTER MILD STROKE AND TRANSIENT ISCHAEMIC ATTACK: PSYCHIATRIC CO-MORBIDITY AND SYMPTOM CLUSTER DISTRIBUTION
Bibliographic record
Abstract
Introduction: Post-traumatic stress disorder (PTSD) is distressing and may be common after stroke and transient ischaemic attack (TIA). A better understanding of the overlap between PTSD with other common post-stroke neuropsychiatric sequelae—depression, anxiety, cognitive impairment can help us develop better psychological support to patients post-stroke/TIA. Method: A secondary analysis of a prospective cohort, of mild stroke and TIA patients followed up at three months using diagnostic psychiatric interview (SCID-DSM-IV) and telephone Montreal Cognitive Assessment (tMOCA). Results: Of 175 participants (mean age 70; 40% women; 65% stroke; 35% TIA), 6% (11/175, 95% CI 3%, 11%) met the diagnostic criteria for PTSD. Almost half of all PTSD cases (5/11) were also diagnosed with depressive episode (minor or major), and all PTSD cases (11/11) were co-morbid with one or more anxiety disorders (phobic disorder or generalized anxiety disorder). Median tMOCA score was 19 and the same in PTSD and non-PTSD cases. PTSD symptom clusters C) ‘persistent avoidance and numbing of general responsiveness’, and D) ‘increased arousal’ were present in more than 10% of our stroke and TIA cohort. Conclusion: Clinical diagnosis of PTSD is present in around 6% of mild stroke and TIA patients at 3 months. There is considerable overlap between PTSD with depression and anxiety disorders. PTSD symptomology is common even in those without PTSD diagnosis, and may indicate subclinical PTSD. Psychological support for stroke and TIA should consider targeting these common symptom clusters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".