Bibliographic record
Abstract
It is well established that individuals with bipolar disorder are differentially affected by substance-related as well as medical disorders (ie, cardiometabolic disorders, respiratory disorders, neurological disorders, and infectious diseases). Emerging evidence indicates that some comorbid conditions (eg, diabetes mellitus) in bipolar individuals may be subserved by overlapping neurobiological networks. Disturbances in glucocorticoid/insulin signaling and immunoinflammatory effector systems are points of pathophysiological commonality between bipolar disorder and “stress-sensitive ” medical disorders. Subphenotyping bipolar disorder as a function of comorbidity and temporality of onset may provide an opportunity for refining disease pathophysiological models and developing innovative disease-modifying therapies. © 2008, LLS SAS Dialogues Clin Neurosci. 2008;10:203-213. ommunity and clinic-based studies have documented a high lifetime prevalence of psychiatric and medical comorbidity in bipolar disorder. For example, the National Comorbidity Survey reported that 95 % of respondents with bipolar disorder also met criteria for three or more additional lifetime psychiatric disorders. 1,2 In keeping with the view that individuals with bipolar disorder are susceptible to comorbid general medical disorders, the Canadian Community Health Survey documented significantly higher rates of cardiometabolic, respiratory, neurological, and infectious disorders in individuals with bipolar disorder. 3 The hazardous effects of psychiatric and medical comorbidity provide the impetus for timely detection, diagnosis, treatment, and management of comorbidity in the bipolar population. For example, co-occurring disorders in bipolar disorder are associated with more severe subtypes (eg, mixed states), an earlier age at onset, an intensification of symptoms, poor symptomatic and functional recovery, suicidal behavior, diminished response to pharmacological treatment, decreased quality of life, as well as an unfavorable course and outcome. 1,4-6 C
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.187 | 0.039 |
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".