Development and validation of the BD SX: a brief measure of mood and symptom variability for use with adults with bipolar disorder
Bibliographic record
Abstract
OBJECTIVES: Ecological momentary sampling in BD research requires brief symptom measures with low cognitive demands to maximize data collection across the range of BD symptomatology. We developed the BD Sx cognizant of the challenges inherent in scale development with low prevalence populations and the limitations of existing measures (e.g., over-reliance on patients in acute states recruited from psychiatric settings). In order to be generalizable across the full spectrum of the illness, we also included those currently euthymic and those who avoid clinical contact. METHODS: We recruited a global sample of 1010 adults with BD over 19 days using socio-demographically targeted, social media advertising and online data collection. At follow-up, 428 participants provided responses 67 days later on average. This enabled us to develop the BD Sx and replicate initial findings across multiple samples over time. RESULTS: Both exploratory and confirmatory factor analyses support a 4-factor BD Sx model. Goodness of fit indices indicate good model fit across samples and over time. We labeled these factors: elation/loss of insight, affrontive symptoms of mania, cognitive/depressive, and somatic/depressive symptoms. Affrontive symptoms correlate positively with cognitive and somatic depression factors, which may suggest mixed-state symptom clusters in accord with DSM 5. CONCLUSIONS: Responses to the BD Sx reliably measure both depressive and hypo/manic symptoms. Temporal invariance analyses indicate that the 4-factor structure is consistent over time. Future research should compare BD Sx responses to clinical diagnoses of hypo/mania and major depressive episodes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".