Prognostic factors in major depressive disorder: comparing responders and non‐responders to Repetitive Transcranial Magnetic Stimulation (<scp>rTMS</scp>), a naturalistic retrospective chart review
Bibliographic record
Abstract
AIM: Repetitive transcranial magnetic stimulation (rTMS) is widely utilized as an effective treatment for major depressive disorder (MDD) with varying response rates. Factors associated with better treatment outcome remain scarce. This naturalistic retrospective chart review hopes to shed light on easily obtainable and measurable predictive factors for patients referred to rTMS. METHODS: Protocol parameters, medication, rated scales, rTMS protocols, and treatment outcomes were reviewed for 196 patients with MDD who received rTMS at Saint Boniface Hospital between 2013 and 2019. Logistic regression and marginal effects were used to assess the different predictor variables for response (50% reduction or more on the Hamilton Depression Rating Scale (Ham-D)) and remission (Ham-D of ≤7 by the last session). RESULTS: HamD at 10 sessions was predictive of remission, and Sheehan Disability Scale (SDS) at 10 sessions was predictive of response to rTMS. Ham-D, SDS, and Beck Anxiety Inventory were predictive of remission and response by Beck Anxiety Inventory 20 sessions. High frequency rTMS had a similar response and remission rate to low frequency, but higher response rate to intermittent Theta Burst Stimulation with no difference in remission rate. Positive predictive factors of response were lower age and bupropion use. Negative predictive factors were antipsychotics, anticonvulsants, or benzodiazepine use. For remission, antipsychotics or anticonvulsants use were negative predictors; bupropion use and higher resting motor threshold were positive predictors. Severity of depression as measured by baseline HamD was not associated with different probabilities of treatment success.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".