Dealing with Posttraumatic Nightmares
Bibliographic record
Abstract
Introduction Posttraumatic nightmares are one of the most frequent symptoms in posttraumatic stress disorder. Prevalence can be up to 96%. These nightmares evoke the experienced traumatic event, causing a negative impact. Besides, they are and independent risk for suicide. There are different pharmacological and non-pharmacological options for PTN, despite is no optimal treatment. Objectives To analyse the different treatment options for PTN. Methods This was a narrative literature review. Results The two main treatments for PTN nowadays are the Imagery Rehearsal Therapy (IRT) and prazosin. IRT is a cognitive-behavioral intervention, that helps the patient to change the content of the nightmare to a “happier ending”. Prazosin is an alpha-adrenergic receptor antagonist that blocks the stress response in the central nervous system receptors. Although it was a promising drug, significant differences compared to placebo have not been found. There is growing data that suggests nabilone, a synthetic cannabinoid, could be helpful in PTN treatment. A clinical trial made in Canada revealed that 72% of patients experienced a complete disappearance or at least an important reduction of PTN. Conclusions PTN is a very common and distressing symptom in patients presenting PTSD. Nevertheless, there is no treatment with enough evidence for this pathology. On this account, it is fundamental to do more research in order to find and suitable treatment that can improve the quality of life of these patients. Disclosure No significant relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".