Cohort profile: the Trivandrum non-alcoholic fatty liver disease (NAFLD) cohort
Bibliographic record
Abstract
PURPOSE: The Trivandrum non-alcoholic fatty liver disease (NAFLD) cohort is a population-based study designed to examine the interaction between genetic and lifestyle factors and their association with increased risk of NAFLD within the Indian population. PARTICIPANTS: Between 2013 and 2016, a total of 2222 participants were recruited to this cohort through multistage cluster sampling across the whole population of Trivandrum-a district within the state of Kerala, South India. Data were collected from all inhabitants of randomly selected households over the age of 25. FINDINGS TO DATE: Full baseline clinical and pathological data were collected from 2158 participants. This included detailed demographic profiles, anthropometric measures and lifestyle data (food frequency, physical activity and anxiety and depression questionnaires). Biochemical profile and ultrasound assessment of the liver were performed and whole blood aliquots were collected for DNA analysis.The NAFLD prevalence within this population was 49.8% which is significantly higher than the global pooled prevalence of 25%. This highlights the importance of robust, prospective studies like this to enable collection of longitudinal data on risk factors, disease progression and to facilitate future interventional studies. FUTURE PLANS: The complete analysis of data collected from this cohort will give valuable insights into the interaction of the phenotypic and genotypic profiles that result in such a dramatic increased risk of NAFLD within the Indian population. The cohort will also form the basis of future lifestyle interventional studies, aimed at improving liver and metabolic health.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".