Using Deep Learning algorithms to detect the success or failure of the Electroconvulsive Therapy (ECT) sessions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Major Depression Disorder (MDD) is a big problem in our society. MDD can cause suicide and take families apart. When treatment with medications fail, mental healthcare professionals, use Electroconvulsive Therapy (ECT) to treat patients with MDD. During an ECT session, electroencephalogram (EEG) signals let the mental healthcare professionals record patients' brain activities which are helpful to decide whether the treatment was successful. However, there is no standard way to know how and with what intensity a healthcare professional needs to apply electroshock to treat patients with MDD. So far, to our knowledge, researchers have used multi-parametric magnetic resonance imaging (MRI) techniques combined with statistical methods and/or linear machine learning algorithms to predict patients’ responses to ECT. However, the aforementioned methods are very expensive and time-consuming. In this study, we will be using Deep learning algorithms to detect the effectiveness of ECT sessions based on the EEG.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it