Anthropometric and Physiological Measures in Individuals With At‐Risk Mental State (<scp>ARMS</scp>) Compared With Individuals With Schizophrenia: Findings From a Lower Middle‐Income Country
Bibliographic record
Abstract
BACKGROUND: Individuals with psychosis have reduced life expectancy and this is largely driven by cardiometabolic disease. Cardiometabolic risk increases with age and duration of psychotic illness. Anthropometric and physiologic abnormalities have been identified among individuals with at-risk mental state (ARMS) for psychosis. The prevalence of cardiometabolic disease is disproportionately higher in lower middle-income countries (LMIC); however, literature on cardiometabolic disease in individuals with psychosis spectrum disorders in LMIC is scarce. METHOD: This is a cross-sectional secondary analysis of data from two large randomised controlled trials that recruited individuals with ARMS (n = 326) and schizophrenia (SCZ; n = 303) from inpatient and outpatient settings in Pakistan. All participants completed anthropometric and physiological assessments. RESULTS: There was a statistically significant difference in BMI between groups, 21.42 (SD = 4.11) in ARMS and 23.31 (SD = 5.41) in the SCZ group (p = 0.001). Although mean values were within the normal range, 17.8% (n = 58) of ARMS individuals and 33.1% (n = 100) SCZ individuals were overweight or obese. Waist circumference was 32.75 in (SD = 3.13) in the ARMS group and 32.16 in (SD = 5.18) in SCZ. Although waist circumference was higher in ARMS, this was not statistically or clinically significant. The pulse rate and blood pressure in both groups were within normal range. CONCLUSION: We found evidence of abnormal anthropometric and physiological parameters that would indicate that individuals with psychotic-spectrum disorders in Pakistan are at an elevated cardiometabolic risk.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".