P-1378 - Brain stimulation techniques: non-invasive treatments for depression
Bibliographic record
Abstract
Nowadays there is an increasing interest on brain stimulation techniques as therapeutic tools in psychiatric disorders. One of the most promising results has been obtained in depression treatment. In fact, some countries have approved the use of transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) as treatments for depression (European Union, Canada, Australia, New Zealand, Unites States and Israel). It is well known that both high frequency repetitive TMS (rTMS) and anodal tDCS over the left dorsolateral pre-frontal cortex (DLPF), are effective in decreasing depressive symptoms and they have lasting beneficial effects. First evidence of benefits of rTMS on depression was published by Pascual-Leone and co-workers in 1996 (Pascual-Leone et al., 1996). These results have been supported by others, both using rTMS (e.g. Anderson et al., 2009; George et al., 2010) and tDCS (e.g. Boggio et al., 2008; Rigonatti et al., 2008). On the other hand, later studies showed that low frequency rTMS over right PFDL cortex is also effective in depression improvement (Kauffmann, Cheema y Miller, 2004). In addition, both techniques have reported good results as treatment of secondary depression associated with Parkinson, epilepsy and brain damage (Fregni et al., 2004; Fregni et al., 2005; Jorge et al., 2004). Recent revisions and meta-analysis corroborates TMS and tDCS efficacy (e.g.: Slotema et al., 2010), always considering that their efficacy depends on several factors, such as the length of the current depression episode, age of patient, length of the treatment, stimulation intensity or number of pulses per session.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.034 | 0.011 |
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".