Trajectories of suicidal ideation during rTMS for treatment-resistant depression
Bibliographic record
Abstract
BACKGROUND: rTMS is a safe and effective intervention for treatment-resistant depression (TRD). However, there is limited data on its specific impact on suicidal ideation (SI), and the trajectory of SI over the treatment course. OBJECTIVE: This open-label clinical trial investigated SI outcomes and trajectories in patients with TRD receiving low-frequency rTMS (LFR) to the right dorsolateral prefrontal cortex (DLPFC; N = 55). METHODS: A latent class mixed-effect model was used to identify response trajectories for SI as well as core mood symptoms. Logistic regression analyses investigated risk factors associated with identified trajectories. RESULTS: For each symptom domain, we identified two distinct trajectories during LFR, one tracking improvement (SI: n = 35, 60 %; mood: n = 29, 53 %) and the other tracking no improvement (SI: n = 20, 40 %; mood: n = 26, 47 %). Male sex, higher baseline anxiety, and higher baseline SI were risk factors for no improvement of SI; while higher baseline anxiety and benzodiazepine use were risk factors for no improvement of mood. Mediation analyses showed that anxiety was a risk factor for no improvement of SI and mood independent of benzodiazepine treatment. CONCLUSIONS: This is the first study to investigate trajectories of response to LFR to the right DLPFC. SI and mood improved with LFR in most patients but the severity of anxiety symptoms was a factor of poor prognosis for both. Nuanced characterization of SI response to rTMS may lead to critical insights for individualized targeting strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".