Staging systems in bipolar disorder: an <scp>I</scp>nternational <scp>S</scp>ociety for <scp>B</scp>ipolar <scp>D</scp>isorders <scp>T</scp>ask <scp>F</scp>orce <scp>R</scp>eport
Bibliographic record
Abstract
OBJECTIVE: We discuss the rationale behind staging systems described specifically for bipolar disorders. Current applications, future directions and research gaps in clinical staging models for bipolar disorders are outlined. METHOD: We reviewed the literature pertaining to bipolar disorders, focusing on the first episode onwards. We systematically searched data on staging models for bipolar disorders and allied studies that could inform the concept of staging. RESULTS: We report on several dimensions that are relevant to staging concepts in bipolar disorder. We consider whether staging offers a refinement to current diagnoses by reviewing clinical studies of treatment and functioning and the potential utility of neurocognitive, neuroimaging and peripheral biomarkers. CONCLUSION: Most studies to date indicate that globally defined late-stage patients have a worse overall prognosis and poorer response to standard treatment, consistent with patterns for end-stage medical disorders. We believe it is possible at this juncture to speak broadly of 'early'- and 'late'-stage bipolar disorder. Next steps require further collaborative efforts to consider the details of preillness onset and intermediary stages, and how many additional stages are optimal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".