Paris MEM: a study protocol for an effectiveness and efficiency trial on the treatment of traumatic stress in France after the 2015–16 terrorist attacks
Bibliographic record
Abstract
BACKGROUND: The Paris and Nice terrorist attacks affected a thousand of trauma victims and first-line responders. Because there were concerns that this might represent the first of several attacks, there was a need to quickly enhance the local capacities to treat a large number of individuals suffering from trauma-related disorders. Since Reconsolidation Therapy (RT) is brief, relatively easy to learn, well tolerated and effective, it appeared as the ideal first-line treatment to teach to clinicians in this context. METHODS: This study protocol is a two-arm non-randomized, multicenter controlled trial, comparing RT to treatment as usual for the treatment of trauma-related disorders. RT consists of actively recalling one's traumatic event under the influence of the ß-blocker propranolol, once a week, for 10-25 min with a therapist, over 6 consecutive weeks. This protocol evaluates the feasibility, effectiveness, and cost-utility of implementing RT as part of a large multi-center (N = 400) pragmatic trial with a one-year follow-up. DISCUSSION: Paris MEM is the largest trial to date assessing the efficiency of RT in the aftermath of a large-scale man-made disaster. RT could possibly reinforce the therapeutic arsenal for the treatment of patients suffering from trauma-related disorders, not only for communities in western countries but also worldwide for terror- or disaster-stricken communities. TRIAL REGISTRATION: Clinical Trials (ClinicalTrials.gov). June 3, 2016. NCT02789982.
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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 | 0.033 | 0.028 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.065 | 0.008 |
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".