2121 – Diffusion Tensor Imaging In Non-resistant Unipolar Major Depression: Preliminary Results
Bibliographic record
Abstract
Major depression is a prevalent condition which typically has a chronic and relapsing course. It is frequently accompanied by changes in brain structure and function, as well as hormonal and inflammatory markers. The relationship of these markers to response remains elusive. A growing literature documents white matter alterations in the right frontal lobe, right fusiform gyrus, left frontal lobe and right occipital lobe as revealed by lower fractional anisotropy in patients with major depression relative to healthy controls. In this pilot study we propose to explore the relationship between white matter integrity in non-resistant major depression and response to treatment. Subjects with unipolar major depression were included. Prior to initiating treatment magnetic resonance imagery was obtained. Cognitive function was evaluated with a computerized neuropsychological battery, and a blood sample for the determination of inflammatory markers was drawn. All subjects are treated with desvenlafaxine 50mg die. A possible option to increase the dose to 100mg die was available at 8 weeks. At 16 weeks the initial evaluation was repeated. We will present preliminary imaging results from the first ten patients. The data from this study may contribute to the incremental increase in evidence clarifying the neuroimmunohormonal factors characterizing depression and response to treatment.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".