Adjunctive Therapy of Text4Support for Treatment-Resistant Depression Patients Receiving Repetitive Transcranial Magnetic Stimulation. A Multicenter Randomized Controlled Pilot Trial
Bibliographic record
Abstract
Introduction Despite several treatment strategies for treatment-resistant depression (TRD) exist, including the use of repetitive transcranial magnetic stimulation (rTMS), new therapeutic options are being introduced. Text4Support is a form of cognitive behavior therapy that allows patients with depression to receive daily supportive text messages that seek to correct or alter negative thought patterns through positive reinforcement. Text4Support is deemed a useful augmentation treatment strategy for patients with TRD. It is however currently unknown if adding the Text4Support intervention will enhance patients with TRD’s response to rTMS treatments Objectives This study aims to assess the initial comparative clinical effectiveness of rTMS when used with and without the Text4Support program as an innovative patient-centered intervention for the management of participants diagnosed with TRD. Methods This study is a multicentered prospective, parallel-design, two-arm, rater-blinded randomized controlled pilot trial. In total, 200 participants diagnosed with TRD will be randomized to one of two treatment arms (rTMS alone and rTMS with Text4Support). Participants in each arm will be made to complete evaluation measures at baseline, 1,3, and 6 months. The primary outcome measure will be the mean change to scores on the Hamilton Depression Rating Scale. Patient service utilization data and clinician-rated measures will also be used to gauge patient progress. Patient data will be analyzed with descriptive statistics, repeated measures, and correlational analyses. Results The result of the study is expected to be available 18 months after the start of recruitment. We hypothesize that participants enrolled in the rTMS plus Text4Support intervention will achieve superior outcomes compared with participants enrolled in the rTMS treatment alone. Conclusions The concomitant application of the combination of these two treatment techniques has not been investigated previously. Therefore, we hope that this project will provide a concrete base of data to evaluate the practical application and efficacy of using a novel combination of these two treatment modalities. Disclosure of Interest None Declared
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".