Evaluation of rTMS for treatment-resistant PTSD and depression in active/retired Canadian Armed Forces and RCMP patients
Bibliographic record
Abstract
Introduction: Repetitive transcranial magnetic stimulation (rTMS) is an effective, non-invasive treatment option for treatment-resistant posttraumatic stress disorder (TR PTSD) and treatment-resistant depression (TRD). This study assesses left-sided high-frequency (LSHF) rTMS on symptoms of Canadian Armed Forces (CAF) and Royal Canadian Mounted Police (RCMP) members with TRD and TR PTSD at the Carewest Operational Stress Injury clinic. Methods: This feasibility study used a quantitative longitudinal design. The study population included 28 CAF and RCMP members with TR PTSD and TRD, defined as lack of response to two antidepressants and one evidence-based psychotherapy. Patients concurrently undertook biological and psychological treatment as usual. Up to 30 sessions of LSHF rTMS were provided; non-responders switched to right-sided low-frequency (RSLF) rTMS after 20 sessions. Depression and PTSD symptoms were quantified using the PTSD Checklist for DSM-5 (PCL-5) and Patient Health Questionnaire-9 (PHQ-9) before treatment, every 10th session, and at completion. Outcomes were analyzed with one-way repeated-measures analysis of variance. Results: < 0.001), reaching sub-clinical threshold (initial mean = 48, 20th session mean = 33, and 30th session, mean = 31). Discussion: These findings support the efficacy of rTMS for reducing symptoms of TRD and TR PTSD in this population. Larger studies with sham rTMS comparison, blinding, and randomization are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 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".