Improvement of Metabolic Risk Profile under Second-Generation Antipsychotics: A Pilot Intervention Study
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of a weight management program on metabolic health of second-generation antipsychotic (SGA)-treated patients. METHODS: A prospective 12-week intervention program including individual exercise training and nutritional group sessions was performed as a pilot study. An intervention group of 6 SGA-treated patients (5 men and 1 woman; mean 15.0, SD 11.8 months) was compared with 10 reference patients under SGAs (8 men and 2 women; mean 14.0, SD 14.2 months), presenting similar age and baseline weekly levels of physical activity. For patients of both groups, anthropometric measurements and basic fasting lipid profile were assessed. For patients in the intervention group, an adapted Rockport Test was performed to evaluate their aerobic fitness and compliance to training sessions, and was recorded. RESULTS: After the 12-week period, reference patients significantly gained weight (P = 0.001), whereas intervention patients showed significant weight loss and decreased body mass index (P = 0.02); interaction between groups: P < 0.01. This weight loss was accompanied by a decreased cholesterol-high-density lipoprotein cholesterol ratio (P = 0.04). Overall, the intervention patients' adherence to exercise prescription was 95.1 %, and this adherence induced a significant improvement of their aerobic fitness (P = 0.05). CONCLUSION: This pilot study suggests that patients under SGAs may benefit from a weight management program and improve their metabolic health, as well as their aerobic fitness.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".