Forecasting the course of bipolar disorder using rest-activity rhythms: Protocol for a multi-study modelling project
Bibliographic record
Abstract
<ns5:p> Background Bipolar disorder (BD) is defined by complex, poorly understood mood dynamics. Our current inability to predict significant changes in mood, particularly episode relapses, is one of the most challenging features of BD for patients and clinicians. Research into sleep and circadian rhythm disruption suggests that relapse risk may be measurable in 24-hour rest-activity rhythms (RAR) collected by actigraphy. This protocol describes a multi-study project with two synergistic aims - to develop an algorithm of relapse risk based on RAR data and to advance mechanistic understanding of sleep and circadian rhythm features of RARs. Method Three prospective studies will enrol adults living with BD I or II. Study 1 (Australia) will recruit a sample of <ns5:italic>N</ns5:italic> = 100 individuals with inter-episode BD who will participate in three contiguous 26-week monitoring epochs. Each epoch commences with 14 days of actigraphy, followed by follow-up interviews at 13 and 26 weeks. RAR predictors of time to first relapse will be explored using Cox’s survival analysis, Bayesian Network and Machine Learning analyses, and Hadaeghi’s Complex System model of BD. Study 2 (India) will recruit 25 individuals with BD and 25 matched healthy controls and replicate one of the 26-week epochs conducted in Study 1. Study 2 tests the replicability of models identified in Study 1 in a lower-middle-income country and investigates the association between amplitude of the RAR and amplitude of the diurnal rhythm in clock gene expression and associated metabolites. Study 3 (New Zealand) will collect actigraphy data from people experiencing a severe episode of BD depression ( <ns5:italic>N</ns5:italic> = 30) or mania ( <ns5:italic>N</ns5:italic> = 15), to model the prediction of treatment response and outcome of acute illness episodes. Discussion The 5-year Tipping Point project uses novel methods and analyses to develop clinically critical inferences about the short-term course of BD. If replicated in future independent samples, the risk forecasting algorithm to be identified here could be translated into practice. </ns5:p>
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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 | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| 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".