CNS volume 15 supplement 8 Cover and Front matter
Bibliographic record
Abstract
Rates of comorbid medical conditions are increased in patients with schizophrenia compared with the general population.Psychiatrists encounter a wide array of medical and psychiatric comorbidities in patients with schizophrenia, including cardiovascular disorders, diabetes, respiratory illnesses, generalized anxiety disorder, and substance use disorders.These comorbidities can contribute to worsened physical health, more severe psychosis and depression, and a greater likelihood of suicide attempts.The treatment of schizophrenia using second-generation antipsychotics can complicate matters further by their propensity for adverse effects such as weight gain and sedation.In this supplement, four experts present cases that revolve around patients with schizophrenia and their comorbidities.Targeted treatment plans and medications are presented, along with the side effects that may be encountered.Also discussed in this supplement are strategies regarding switching antipsychotics and adding augmenting agents, as well as the use of nonpharmacologic interventions, such as behavioral therapy.Leslie Citrome, MD, MPH, presents a case of an obese female patient who was developing chronic obstructive pulmonary disease from smoking.Oliver Freudenreich, MD, presents a second case of a male patient with a 16-year history of schizoaffective disorder who gained considerable weight on antipsychotics.Peter J. Weiden, MD, and Michael Weiden, MD, present two cases, the first of a patient with asthma, and the second of a patient with substance use who develops an acute dystonic reaction after receiving an intramuscular dose of an antipsychotic when being treated in the emergency room.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.581 | 0.299 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".