Affective lability as a prospective predictor of subsequent bipolar disorder diagnosis: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: The early pathogenesis and precursors of Bipolar Disorder (BD) are poorly understood. There is some cross-sectional and retrospective evidence of affective lability as a predictor of BD, but this is subject to recall biases. The present review synthesises the prospective evidence examining affective lability and the subsequent development of BD at follow-up. METHODS: The authors performed a systematic search of PubMed, PsycInfo and Embase (1960-June 2020) and conducted hand searches to identify studies assessing affective lability (according to a conceptually-inclusive definition) at baseline assessment in individuals without a BD diagnosis, and a longitudinal follow-up assessment of bipolar (spectrum) disorders. Results are reported according to the PRISMA guidelines, and the synthesis without meta-analysis (SWiM) reporting guidelines were used to strengthen the narrative synthesis. The Newcastle-Ottawa Scale was used to assess risk of bias (ROB). RESULTS: 11 articles describing 10 studies were included. Being identified as having affective lability at baseline was associated with an increased rate of bipolar diagnoses at follow-up; this association was statistically significant in six of eight studies assessing BD type I/II at follow-up and in all four studies assessing for bipolar spectrum disorder (BSD) criteria. Most studies received a 'fair' or 'poor' ROB grade. CONCLUSIONS: Despite a paucity of studies, an overall association between prospectively-identified affective lability and a later diagnosis of BD or BSD is apparent with relative consistency between studies. This association and further longitudinal studies could inform future clinical screening of those who may be at risk of BD, with the potential to improve diagnostic accuracy and facilitate early intervention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it