Transcranial Direct Current Stimulation (tDCS) for Depression during Pregnancy: Scientific Evidence and What Is Being Said in the Media—A Systematic Review
Bibliographic record
Abstract
Major depression is the most frequent morbidity in pregnancy. The first-line therapies, psychopharmacologic treatment and psychotherapy, are either insufficient or may cause severe or teratogenic adverse events. As a result of its local limitation to the patient's brain, transcranial direct current stimulation (tDCS) could potentially be an ideal treatment for pregnant women with depression. A literature search was conducted in medical databases, globally published newspapers, search engines, and clinical trial registers to collect all articles on tDCS for the treatment of depression during pregnancy. The aim of this review was to investigate the scientific evidence of tDCS use for depression during pregnancy and to compare these results with the textual and emotional perception in the media as interventions during pregnancy are under particular surveillance. We detected 13 medical articles dealing with tDCS for depression in pregnancy. Overall, the scientific evidence as well as articles in the media for tDCS in pregnancy are sparse, but promising. Further studies are required in this specifically vulnerable population of pregnant women to generate evidence. It is likely that public interest will increase when the results of a pilot study in Canada are published.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".