A Novel Unsupervised Machine Learning Approach to Assess Postural Dynamics in Euthymic Bipolar Disorder
Bibliographic record
Abstract
Bipolar disorder (BD) is a mood disorder with different phases alternating between euthymia, manic or hypomanic episodes, and depressive episodes. While motor abnormalities are commonly seen during depressive or manic episodes, not much attention has been paid to postural abnormalities during periods of euthymia and their association with illness burden. We collected 24-hour posture data in 32 euthymic participants diagnosed with BD using a shirt-based wearable. We extracted a set of nine time-domain features, and performed unsupervised participant clustering. We investigated the association between posture variables and 12 clinical characteristics of illness burden. Based on their postural dynamics during the daytime, evening, or nighttime, participants clustered in three clusters. Higher illness burden was associated with lower postural variability, in particular during daytime. Participants who exhibited a mostly upright sitting/standing posture during the night with frequent nighttime postural transitions had the highest number of lifetime depressive episodes. Euthymic participants with BD exhibit postural abnormalities that are associated with illness burden, especially with the number of depressive episodes. Our results contribute to understanding the role of illness burden on posture changes and sleep consolidation in periods of euthymia.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".