Reduction of PTSD Symptoms With Pre-Reactivation Propranolol Therapy: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: The authors assessed the efficacy of trauma memory reactivation performed under the influence of propranolol, a noradrenergic beta-receptor blocker, as a putative reconsolidation blocker, in reducing symptoms of posttraumatic stress disorder (PTSD). METHOD: This was a 6-week, double-blind, placebo-controlled, randomized clinical trial in 60 adults diagnosed with long-standing PTSD. Propranolol or placebo was administered 90 minutes before a brief memory reactivation session, once a week for 6 consecutive weeks. The hypothesis predicted a significant treatment effect of trauma reactivation with propranolol compared with trauma reactivation with placebo in reducing PTSD symptoms on both the Clinician-Administered PTSD Scale (CAPS) and the patient-rated PTSD Checklist-Specific (PCL-S) in an intention-to-treat analysis. RESULTS: The estimated group difference in posttreatment CAPS score, adjusted for pretreatment values (analysis of covariance), was a statistically significant 11.50. The within-group pre- to posttreatment effect sizes (Cohen's d) were 1.76 for propranolol and 1.25 for placebo. For the PCL-S, the mixed linear model's estimated time-by-group interaction yielded an average decrease of 2.43 points per week, for a total significant difference of 14.58 points above that of placebo. The pre- to posttreatment effect sizes were 2.74 for propranolol and 0.55 for placebo. Per protocol analyses for both outcomes yielded similar significant results. CONCLUSIONS: Pre-reactivation propranolol, a treatment protocol suggested by reconsolidation theory, appears to be a novel and efficacious treatment for PTSD. Replication studies using a long-term follow-up in various trauma populations are required.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".