Guest Editorial Metabolic Effects of Antipsychotic Treatment: Between a Rock and a Hard Place?
Bibliographic record
Abstract
t often feels as though medicine is practised “between a rock and a hard place. ” As physicians, we make difficult treatment decisions that can profoundly affect the lives of our patients. This is particularly true with the present generation of antipsychotic medications, where we have to deal with the paradox that some of our best medications are associated with the greatest metabolic side effects (1). With our patients and their families, we face the dilemma of seeing improvement in psychotic symptoms accompanied by significant weight gain, lipid disturbance, and occasionally, emergent diabetes. We appreciate that our primary goal is to treat psychiatric illness, but do we need to accept these side effects as inevitable and unavoidable? How do we understand and how do we manage metabolic risk when we treat psychosis? Are we truly keeping in mind the long-term interests of our patients? We have learned that patients with schizophrenia and other forms of severe mental illness have high rates of medical comorbidity (2) and that their life expectancy is shortened, primarily as a consequence of increased coronary heart dis-ease mortality (3). There are barriers to accessing medical care related both to psychiatric illness (for example, self-neglect and difficulty in communicating symptoms) and to the health care system (for example, lack of integrated mental and physi-cal health care). This has been aptly described as “duel neglect by patients and the system ” (4, p 1). Psychiatrists are physi-cians first. What is our responsibility here? How far should we extend our scope of practice? In this issue of The Canadian Journal of Psychiatry, 3 review papers offer an overview of our present state of knowledge in this area, with the ambitious goal of informing and shaping clinical practice. In the first paper, John Newcomer and Dan Haupt from the Washington University School of Medicine review the meta-bolic effects of antipsychotic treatment (5). John Newcomer is
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".