Frequency of metabolic syndrome in psychiatric patients, is this the time to develop a standardized protocol to reduce the morbidity from an acute care psychiatry unit.
Bibliographic record
Abstract
OBJECTIVE: To determine the frequency of Metabolic Syndrome among psychiatric patients and to look for the correlation between the two medical conditions. METHODS: The cross-sectional study was conducted from February to April 2013 at the acute care psychiatry in-patient unit at Kingston General Hospital, Ontario, Canada, and comprised adult patients of both genders diagnosed under the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. For Metabolic Syndrome, definitions outlined by the International Diabetes Federation were used. The patients were divided into two groups on the basis of presence or absence of the Syndrome and were compared for clinical and demographic characteristics. SPSS 22 was used for statistical analysis. RESULTS: Of the 50 patients in the study, 24(48%) were found to have Metabolic Syndrome. Besides, 40 (80%) patients were taking atypical antipsychotics regardless of the diagnosis; 20(83%) among those with the Syndrome, and 20(77%) among those without it. CONCLUSIONS: Patients at high risk of developing metabolic syndrome need to be identified early so that an individualised care plan can be formulated. Identifying the variables to make a management plan is vital.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".