A patient-oriented analysis of pain side effect: A step to improve the patient's experience during rTMS?
Bibliographic record
Abstract
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an efficacious and well-tolerated intervention for treatment-resistant depression (TRD). A novel rTMS protocol, intermittent theta burst stimulation (iTBS) has been recently implemented in clinical practice, and it is essential to characterize the factors associated to pain and the trajectory of pain of iTBS compared to standard rTMS protocols. OBJECTIVE: We aimed to characterize the side effect profile and the pain trajectories of High-Frequency Left (HFL) and iTBS in TRD patients in the THREE-D trial. We also investigated factors associated to pain and the relationship between pain and clinical outcomes. METHODS: 414 patients were randomized to either HFL or iTBS. Severity of pain was measured after every treatment. General Estimating Equation was used to investigate factors associated with pain. Latent class linear mixed model was used to investigate latent classes of pain trajectories over the course of rTMS. RESULTS: Higher level of pain was associated with older age, higher stimulation intensity, higher anxiety, female, and non-response. The severity of pain significantly declined over the course of treatments with a steeper decrease early on in the course of the treatment in both protocols, and four latent pain trajectories were identified. The less favorable pain trajectories were associated with non-response and higher stimulation intensity. CONCLUSIONS: HFL and iTBS were associated with similar factors and pain trajectories, although iTBS was more uncomfortable. Response to rTMS was associated with less pain and more favorable pain trajectories furthering the evince base of overlapping neurobiological underpinnings of mood and pain. We translated these results into patient-oriented information to aid in the decision-making process when considering rTMS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".