Nightmare Frequency, Nightmare Distress and the Efficiency of Trauma-Focused Cognitive Behavioral Therapy for Post-Traumatic Stress Disorder
Bibliographic record
Abstract
BACKGROUND: Up to 71% of trauma victims diagnosed with PTSD have frequent nightmares (NM), compared to only 2% to 5% of the general population. OBJECTIVES: The present study examined whether nightmares before the beginning of cognitive behavior therapy (CBT) for post-traumatic stress disorder (PTSD) could influence overall PTSD symptom reduction for 71 individuals with PTSD and different types of traumatic events. PATIENTS AND METHODS: Participants received a validated CBT of 20 weekly individual sessions. They were evaluated at five measurement times: at pre-treatment, after the third and ninth session, at post-treatment, and at 6 months follow-up. RESULTS: The presence of nightmares did not impact overall CBT efficiency. Specific CBT components were efficient in reducing the frequency and distress of nightmares. CONCLUSIONS: Most participants no longer had PTSD but some still had nightmares.
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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 | 0.001 | 0.005 |
| 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) | 0.001 | 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".