Atypical Antipsychotics in Psychiatric Practice: Practical Implications for Clinical Monitoring
Bibliographic record
Abstract
OBJECTIVES: To provide practical recommendations for monitoring patients both before and during treatment with atypical antipsychotics, to assist clinicians in implementing preventative measures against diabetes, and to establish baselines according to which clinicians should initiate diabetes treatment. METHOD: A working group of Canadian specialists in psychiatry and endocrinology reviewed peer-reviewed clinical studies published in this area and other relevant papers and abstracts. RESULTS: The reviewed studies further confirm that atypical antipsychotic medications are the most effective components in the medical management of many psychotic conditions; they also further emphasize the need to more stringently monitor and recognize diabetes risk factors inherent in these patients. Recommendations are based on a review of the available data, on expert opinion and consensus, and on current Canadian guidelines for the treatment of schizophrenia and management of diabetes. CONCLUSIONS: Patients with psychiatric disorders, most particularly schizophrenia and mood disorders, have an increased risk for type 2 diabetes and should be screened frequently, especially when other risk factors are present. The resulting recommendations offer practical steps for effectively screening patients prior to and during treatment with atypical antipsychotics. They include (1) how to conduct an initial baseline assessment, (2) when and how to monitor blood glucose and lipid levels, and (3) how to educate patients regarding such lifestyle issues as nutrition, exercise, and diet.
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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".