Approximate entropy of self‐reported mood prior to episodes in bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Approximate entropy (ApEn) measures regularity in time series data, while traditional linear statistics measure variability. Using self-reported mood data from patients with bipolar disorder, this preliminary study addressed whether ApEn could distinguish (i) the 60 days prior to the start of a manic or depressed episode from the 60 days prior to a month of euthymia, and (ii) the 60 days prior to a manic episode from the 60 days prior to a depressed episode. METHODS: Self-reported mood data from 49 outpatients with bipolar disorder receiving standard treatment were analysed. The data contained 27 episodes (12 manic and 15 depressed), and 43 periods of 1 month of euthymia. For the 60 days prior to episode or euthymia, the ApEn, linear statistics and the correlation between linear and non-linear measures were calculated. RESULTS: ApEn was significantly greater in the 60 days prior to a manic or depressive episode than the 60 days prior to a month of euthymia. The onset of an episode was associated with greater irregularity in mood. Variability was also significantly larger and correlated with ApEn. ApEn was significantly greater in the 60 days prior to a manic episode than in the 60 days prior to a depressed episode, whereas measures of variability were not significantly different. Mood in the 60 days prior to mania was more irregular than prior to depression. CONCLUSIONS: Non-linear measures may complement traditional linear measures in the analysis of longitudinal data in bipolar disorder. A larger study 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".