Change in mood instability (MI) with time and treatment
Bibliographic record
Abstract
Introduction The literature indicates that most patients with Major Depression mention sudden short “mood swings” (MI) when asked. MI is known to be distressing but little is known about the treatment. Objectives To determine whether MI changes with community treatment of depression. Aim To study changes in MI with 3-6 months of treatment for depression in patients with Major Depression and complaints of MI. Methods 34 patients with Major Depression and complaints of “mood swings” were recruited from 4 psychiatric practices. They were interviewed with the MINI diagnostic interview and the Mood Disorders Questionnaire. They completed the Beck Depression Inventory (BDI), the State-Trait Anxiety Inventory Trait Form (STAI-T), and Visual Analogue Scales for Depressed Mood (VAS) and Anxious Mood twice a day for a week. The Mean Square Successive Difference Statistic (MSSD) was calculated from the VAS readings. The BDI, STAI-T, and VAS were repeated after 6 months of treatment. Results 25/34 patients reported past hypomania. Most patients were treated with a combination of antidepressants and mood stabilizers. The BDI and STAI-T scores improved with treatment. There was no overall change in depressed and anxious MI. Change in Depressed MI and Anxiety MI correlates with change in BDI from T1 to T2 Conclusions Anxiety and depression improved with treatment as expected. Change in MI is inconsistent. Research into medications and psychosocial treatments that improve MI is needed and this will probably improve depression treatment outcome.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".