Trauma reactivation under the influence of propranolol: an examination of clinical predictors
Bibliographic record
Abstract
BACKGROUND: In two recent studies conducted by our group, a treatment combining propranolol with a brief reactivation session subsequently reduced posttraumatic stress disorder (PTSD) symptom severity and diagnosis, as well as reducing psychophysiological responses during trauma-related script-driven imagery. One likely explanation for those results is that memory reconsolidation was blocked by propranolol. OBJECTIVE: We explored the influence of various predictors on treatment outcome (i.e., PTSD severity), and whether the treated individuals improved in other important domains of functioning associated with PTSD. METHOD: Thirty-three patients with longstanding PTSD participated in a 6-week open-label trial consisting of actively recalling one's trauma under the influence of propranolol, once a week. RESULTS: Treated patients reported a better quality of life, less comorbid depressive symptoms, less negative emotions in their daily life and during trauma recollections. Women were also found to improve more than men. Type of trauma (childhood vs. adulthood), time elapsed since trauma, borderline personality traits, depressive symptoms severity, Axis I comorbidity, and age did not influence treatment outcome. CONCLUSION: These results must await publication of a randomized-controlled trial to further delineate effectiveness with this novel treatment approach.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".