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Record W4377090266 · doi:10.1136/heartjnl-2023-322397

Mortality trends of aortic stenosis in high-income countries from 2000 to 2020

2023· article· en· W4377090266 on OpenAlexaffabout
Makoto Hibino, Arjun Pandey, Hiromi Hibino, Raj Verma, Dagfinn Aune, Bobby Yanagawa, Yoshiyuki Takami, Deepak L. Bhatt, Guilherme F. Attizzani, Marc Pelletier, Subodh Verma

Bibliographic record

VenueHeart · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsMcMaster UniversityUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineDemographyMortality rateAge groupsSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study is to describe recent mortality trends from aortic stenosis (AS) among eight high-income countries. METHODS: We analysed the WHO mortality database to determine trends in mortality from AS in the UK, Germany, France, Italy, Japan, Australia, the USA and Canada from 2000 to 2020. Crude and age-standardised mortality rates per 100 000 persons were calculated. We calculated age-specific mortality rates in three groups (<64, 65-79 and ≥80 years). Annual percentage change was analysed using joinpoint regression. RESULTS: During the observation period, the crude mortality rates per 100 000 persons increased in all the eight countries (from 3.47 to 5.87 in the UK, from 2.98 to 8.93 in Germany, from 3.84 to 5.52 in France, from 1.97 to 4.33 in Italy, from 1.12 to 5.49 in Japan, from 2.14 to 3.38 in Australia, from 3.58 to 4.22 in the USA and from 2.12 to 5.00 in Canada). In joinpoint regression of age-standardised mortality rates, trend changes towards a decrease were observed in Germany after 2012 (-1.2%, p=0.015), Australia after 2011 (-1.9%, p=0.005) and the USA after 2014 (-3.1%, p<0.001). Age-specific mortality rates in age group ≥80 years had shifts towards decreasing trends in all the eight countries in contrast to other younger age groups. CONCLUSIONS: While crude mortality rates increased in the eight countries, shifts towards decreasing trends were identified in age-standardised mortality rates in three countries and in the elderly aged ≥80 years in the eight countries. Further multidimensional observation is warranted to clarify the mortality trends.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.354
Teacher spread0.336 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

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