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Record W4395449878 · doi:10.1016/j.puhip.2024.100500

Exploring the determinants associated with adult mortality in Malta: A cohort study between 2014 and 2020

2024· article· en· W4395449878 on OpenAlexaff
Sarah Cuschieri

Bibliographic record

VenuePublic Health in Practice · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsWestern University
FundersStrong
KeywordsMedicinePopulationLogistic regressionDemographyCohortAcute coronary syndromeCause of deathLung cancerEpidemiologyMyocardial infarctionDiseaseInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.135
GPT teacher head0.407
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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