Comparison of Pre-episode and Pre-remission States Using Mood Ratings from Patients with Bipolar Disorder
Bibliographic record
Abstract
Daily self-reported mood ratings from patients with bipolar disorder were analyzed to see if the 60 days before an episode of hypomania or depression (pre-episode state) could be distinguished from the 60 days before a month of euthymia (pre-remission state), and if a pre-hypomanic state could be distinguished from a pre-depressed state. Data were available from 98 outpatients with bipolar disorder, who returned about one year of daily data, and received treatment as usual. The approximate entropy (ApEn), mean mood and mood variability (standard deviation) were determined for 53 pre-hypomanic states, 42 pre-depressive states, and 65 pre-remission states.There was greater serial irregularity (ApEn) and greater variability in mood in the pre-episode than the pre-remission state. There was greater serial irregularity (ApEn) but no difference in variability in mood in the pre-hypomanic state when compared to the pre-depressed state. ApEn can distinguish between the pre-episode, pre-remission, pre-hypomanic and pre-depressive states. Using daily mood ratings, pre-episode changes were detected before the episode onset. Further investigation to relate the pre-episode and pre-remission states to other clinical and biological data is indicated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".