Alarm of non-communicable disease in Iran: Kavar cohort profile, baseline and 18-month follow up results from a prospective population-based study in urban area
Bibliographic record
Abstract
The PERSIAN Kavar cohort study (PKCS) aims to investigate the prevalence, trends, and relevant prognostic risk factors of non-communicable diseases in participants aged 35-70 years living in the urban area of Kavar County. Kavar County is located at the center of Fars province in the southwest of Iran. Overall, 5236 adults aged 35-70 years old were invited to participate in the PKCS. From whom, 4997 people comprising 2419 men and 2578 women met the inclusion criteria and were recruited in the study (participation rate: 95.4%). This study is aimed to follow participants for at least 10 years; it is designed to perform all procedures similar to the primary phase including biological sampling, laboratory tests, physical examinations, and collecting general, nutritional, and medical data at the 5th and 10th years of follow-up. In addition, participants are annually followed-up by phone to acquire data on the history of hospitalization, any major diagnosis or death. At the enrollment phase, trained interviewers were responsible for obtaining general, nutritional, and medical data utilizing a 482-item questionnaire. The results of the baseline phase of this study show that the overweight category was the most prevalent BMI category among the registered participants (n = 2005, 40.14%). Also, almost one-third of Kavar adult population suffered from metabolic syndrome at the baseline phase (n = 1664, 33.30%). The rate of eighteen-month follow-up response was 100% in the PKCS. Hypertension (n = 116, 2.32%), cardiovascular outcomes (n = 33, 0.66%), and diabetes (n = 32, 0.64%) were the most prevalent new-onset NCDs during eighteen months of follow-up in the participants.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".