In Reply: Adverse Effects of Deep Brain Stimulation for Treatment-Resistant Depression: A Scoping Review
Bibliographic record
Abstract
To the Editor: We thank Barrie and Detchou1 for their letter commenting on our article “Adverse effects of deep brain stimulation for treatment resistant depression: A scoping review.”2 As stated by the authors, their objective was to present additional ethical considerations on deep brain stimulation (DBS) for depression. Barrie and Detchou largely comment on issues unrelated to our review, including the efficacy of DBS, access to care, the need to identify treatment goals, patient competency, and informed consent. In the few instances in which side effects were examined, these were discussed from an ethical standpoint. Some of the examples provided to illustrate their opinion were extracted from noninvasive neuromodulation work or studies using DBS to treat neuropsychiatric disorders other than depression. Ethical concerns are certainly valid and extremely important but were not the subject of our review. As noted in the letter, a direct and comprehensive conversation with patients undergoing surgery about the risks and potential benefits of the procedure is necessary. Data on efficacy may be retrieved from clinical trials and the various reviews published in the literature. By contrast, estimates on safety have largely been provided based on clinical trials and studies on the adverse effects of DBS for other conditions, including movement disorders. Our aim was to cover this gap and summarize the incidence of side effects of DBS in patients with depression. Off-Label Statement: DBS is not approved by the US Food & Drug Administration for the treatment of depression.
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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 | 0.016 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.028 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".