Exploring the determinants associated with adult mortality in Malta: A cohort study between 2014 and 2020
Bibliographic record
Abstract
The study set to explore the mortality causes across six years and identify potential mortality determinates at a population level in Malta. A longitudinal follow-up of a Malta based cross-sectional national representative study across 6 years (2014 – 2020) was carried out. The study population was cross-linked to the mortality register and causes of death obtained. Population characteristics gathered during initial examination were analysed through univariant and multivariant logistic regressions. A total of 66 adults, mostly male (65.15% n=43) died, with commonest cause being cancer (42.42% CI95%: 31.24 – 54.45) mostly due to malignant neoplasm of bronchus and lung. This was followed by cardiac pathologies including acute myocardial infarction, ischaemic cardiomyopathy, and cardiomegaly (25.76% CI95%: 16.67 – 37.51). Multivariant logistic regression analyses revealed positive associations between age (OR: 1.99 p=0.02), history of coronary heart disease (OR: 11.78 p=<0.001), smoking for 31 years or more (OR: 8.22 p=<0.001) and presence of multimorbidity (OR: 1.32 p=0.02). It is evident that occurrence of cancers is a concern in Malta, and it requires targeted action including the reduction of smoking habits. Understanding the mortality causes and the associated determining factors at a population level enable the institution of preventive actions while strengthening healthcare services to safeguard the population from premature mortality and co-morbidity.
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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 | 0.000 | 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 | 0.001 |
| 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".