Bibliographic record
Abstract
OBJECTIVES: To review the literature evaluating outcomes resulting from expansion of the bipolar disorder (BD) diagnostic category. We were particularly interested in identifying high-level evidence for improved clinical outcomes as documented by randomized controlled trials (RCTs) or cohort studies. METHODS: The English-language literature was searched using Ovid MEDLINE for studies of BD referenced against the key word spectrum. We used bibliographies and other databases to extend this search when no relevant RCTs or relevant cohort studies were identified. RESULTS: In the MEDLINE searches, abstracts and titles of 86 studies were examined and 48 were found to be related to the topic of bipolar spectrum disorders (BSD). No RCTs or prospective cohort studies evaluating modified diagnostic or therapeutic practices were identified. The literature about the BSD consists mostly of expert opinion emphasizing: various links between bipolar and unipolar mood disorders; a proposal that a greater proportion of the population without a mood disorder as defined by the Diagnostic and Statistical Manual of Mental Disorders should be diagnosed under the BD category; and, proposals that syndromes currently classified elsewhere should be subsumed under the BD category. CONCLUSIONS: Our search failed to uncover high-level evidence demonstrating the clinical utility of proposed diagnostic realignments. The widespread acceptance of the expanded spectrum concept appears to be based on interpretation of descriptive epidemiologic data by high-profile experts.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".