Predictors of remission after repetitive transcranial magnetic stimulation for the treatment of major depressive disorder: An analysis from the randomised non-inferiority THREE-D trial
Bibliographic record
Abstract
BACKGROUND: Although repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for major depressive disorder (MDD), treatment selection is still mainly a process of trial-and-error. The present study aimed to identify clinical predictors of remission after a course of rTMS delivered to the left DLPFC to improve patient selection. METHODS: Data from a large randomised non-inferiority trial comparing standard 10 Hz and intermittent theta burst stimulation (iTBS) for the treatment of MDD were used for the exploratory analyses. Individual variables were assessed for their association with remission and then included in a logistic regression model to determine odds ratios (OR) and corresponding 95% confidence intervals. Model discrimination (internal validation) was carried out to assess model optimism using the c-index. ClinicalTrials.gov identifier: NCT01887782. FINDINGS: 388 subjects were included in the analysis (199-iTBS and 189-10 Hz, respectively). Higher baseline severity of both depressive and anxiety symptoms were associated with a lower chance of achieving remission (OR=0.64, 95% CI 0.46-0.88; and 0.78, 95% CI 0·60-0.98, respectively). Current employment was a positive predictor for remission (OR=1.69, 95% CI 1.06-2.7), while greater number of treatment failures was associated with lower odds of achieving remission (OR=0.51, 95% CI 0.27-0.98). A non-linear effect of age and remission was observed. An analysis to allow an estimate of the probability of remission using all variables was assessed. The c-index for the fitted model was 0.687. INTERPRETATION: Our results suggest that measuring depression symptom severity, employment status, and refractoriness are important in prognosticating outcome to a course of rTMS in MDD. FUNDING: Canadian Institutes of Health Research MOP-136801.
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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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".