Pure and mixed manic subtypes: a review of diagnostic classification and validation
Bibliographic record
Abstract
OBJECTIVE: To review issues surrounding the diagnosis and validity of bipolar manic states. METHODS: Studies of the manic syndrome and its diagnostic subtypes were reviewed emphasizing historical development, conceptualizations, formal diagnostic proposals, and validation. RESULTS: Definitions delineating mixed and pure manic states derive some validity from external measures. DSM-IV and ICD-10 diagnosis of bipolar mixed states are too rigid and less restrictive definitions can be validated. Anxiety is a symptom often overlooked in diagnosis of manic subtypes and may be relevant to the mixed manic state. The boundary for separation of mixed mania and depression remains unclear. A 'pure' non-psychotic manic state similar to Kraepelin's 'hypomania' has been observed in several independent studies. CONCLUSIONS: Issues surrounding diagnostic subtyping of manic states remain complex and the debates surrounding categorical versus dimensional approaches continue. To the extent that categorical approaches for mixed mania diagnosis are adopted, both DSM-IV and ICD-10 are too rigid. Inclusion of non-specific symptoms in definitions of mixed mania, such as psychomotor agitation, does not facilitate and may hinder the diagnostic separation of pure and mixed mania. The inclusion of a diagnostic seasonal specifier for DSM-IV, which is currently based on seasonal patterns for depression might be expanded to include seasonal patterns for mania. Boundaries between subtypes may be 'fuzzy' rather than crisp, and graded approaches could be considered. With the continued development of new tools, such as imaging and genetics, alternative approaches to diagnosis other than the purely symptom-centric paradigms might be considered.
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 | 0.025 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".