Bibliographic record
Abstract
Bipolar Disorder Bipolar Disorder in Later Life Martha Sajatovic and Frederic C Blow, editors. Baltimore (MD): The Johns Hopkins University Press; 2007. 257 p. US$50.00 Reviewer rating: Excellent Review by Kenneth Shulman, MD Toronto, Ontario One of the chapters in this comprehensive and timely text on bipolar disorder in later life uses the apt subtitle, Navigating Terra Incognita. This is an underlying theme to the entire text, as the evidence base for understanding the nature and management of bipolar disorders in later life is very limited. Nonetheless, the authors have compiled this text because late-life bipolar disorders are associated with poor social outcomes, significant medical comorbidities, and increased health care costs. The book is carefully edited by Martha Sajatovic, a well-known American researcher in late-life bipolar disorders, and Frederic Blow, director of the National Serious Mental Illness Treatment Research and Evaluation Center in the Veterans Affairs System in Michigan. They bring together a vast amount of research and clinical experience, directly and indirectly related to bipolar disorders in older adults. The text is divided into 4 sections including: epidemiology and assessment, treatment, complexity and comorbidity, and specialized care delivery and research. One of the prominent themes and issues that emerge is the question of burnout and age of onset of later life bipolar disorders. The authors argue that these disorders do not burnout, but remain a significant concern in later life. Really, the question is: Do early onset bipolar disorders burnout as they grow old? For this the evidence is quite mixed depending on whether one uses an outpatient or inpatient sample. However, the authors make the valid point that the burden of bipolar disorder in old age does not burnout or diminish. Sajatovic and colleagues used a large Veteran's Affairs database in the United States to document the heavy cost of health services by older bipolar patients, including their medical and psychiatric comorbidities. Curiously, they found that early onset patients in the Veteran's Affairs database seem to create a greater burden than late onset cases. Just why this should be adds yet another important and unanswered question to the many posed in this book. Age of onset remains an important variable that helps to identify different factors relevant to the pathogenesis and etiology of bipolar disorders across the lifespan. Perhaps the best chapter in the book is the one written by the editors themselves: Epidemiology of Bipolar Disorder in Later Life. This is a concise and well-written chapter that addresses gender differences, outcomes in the use of health services, clinical presentation, comorbidity, barriers to care, and treatment. Each of these subsections is addressed more completely in separate chapters. The book highlights numerous interesting and unique approaches to understanding bipolar disorders. …
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.021 |
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".