Early symptom improvement at 10 sessions as a predictor of rTMS treatment outcome in major depression
Bibliographic record
Abstract
BACKGROUND: Predicting rTMS nonresponse could be helpful in sparing patients from futile treatment, and in improving use of limited rTMS resources. While several predictive biomarkers have been proposed, few are accurate for individual-level prediction; none have entered routine use. An alternative approach in pharmacotherapy predicts outcome from early response; patients showing minimal (e.g., ≤20%) improvement at 2 weeks can be predicted as nonresponders with negative predictive values (NPV) > 80-90%. This approach has recently been extended to ECT, but never before to rTMS. OBJECTIVE: To assess the accuracy of 2-week clinical response in predicting rTMS treatment outcome. METHODS: We reviewed clinical symptom scores for 101 patients who underwent 20 sessions of dorsomedial prefrontal rTMS for unipolar major depression in a naturalistic retrospective case series, defining nonresponders both at the conventional <50% improvement criterion and at a more stringent <35% criterion. RESULTS: Patients achieving <20% improvement at session 10 were correctly predicted as nonresponders with NPVs of 88.2% by the conventional and 80.4% by the stringent criterion. Achieving <10% improvement at session 10 predicted nonresponse with NPVs of 89.5% and 86.8% by conventional and stringent criteria, respectively. Using the least-depressed score of either session 5 or 10, <20% improvement predicted nonresponse with NPVs of 91.3% and 82.6%, and <10% improvement predicted nonresponse with NPVs of 93.5% and 93.5%, by conventional and stringent criteria. CONCLUSION: For DMPFC-rTMS, a '<20% improvement at 2 weeks' rule concurred with previous pharmacotherapy and ECT studies on predicting nonresponse, and could prove useful for treatment decision-making in clinical settings.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".