MétaCan
Menu
Back to cohort
Record W3093487865 · doi:10.36141/svdld.v37i1.7660

The incidence, comorbidity and mortality of sarcoidosis in Korea, 2008-2015: a nationwide population-based study.

2020· article· en· W3093487865 on OpenAlexaff
Mi Hye Jeon, Taeuk Kang, Sang Hoon Yoo, Heather Swan, Hyun Jung Kim, Hyeong Sik Ahn

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldMedicine
TopicSarcoidosis and Beryllium Toxicity Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineComorbidityEpidemiologyIncidence (geometry)SarcoidosisPopulationMortality rateStandardized mortality ratioInternal medicineDiseaseConfidence intervalPediatricsEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Few national level, population-based studies are present on the epidemiology of sarcoidosis and it is unclear whether these patients have higher mortality than the general population. The objective of this study was to investigate the nationwide epidemiology, comorbidity and mortality in sarcoidosis in Korea. MATERIAL AND METHODS: For the period between 2008 to 2015, we used the national population-based database operated by Rare Intractable Disease registration program in which patients' diagnosis are based on uniform criteria. All sarcoidosis patients were identified and followed-up using the National Health Insurance database to determine their incidence, comorbidity, mortality, causes of death and standardised mortality ratio (SMR). RESULTS: During the study period, we identified 3,259 new sarcoidosis patients. The average annual incidence was 0.81 per 100,000. The annual mortality rate was 9.26 per 1,000 person-years. The mortality rate were significantly higher than those of the general population (SMR 1.91, 95% confidence interval 1.62-2.25). The major comorbidities of sarcoidosis patients were the diseases of the respiratory system (17.64%), heart (5.43%), eyes (4.27%) and cancer (2.3%). Mortality was higher in patients with lung involvement. Of the 84 deaths identified in this study from 2008-2013, the most common cause of death was cancer (41.7%), followed by respiratory disease (13.1%), sarcoidosis (13.1%) and heart disease (8.3%). CONCLUSIONS: .

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)0.001
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.073
GPT teacher head0.332
Teacher spread0.259 · 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

Citations26
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicSarcoidosis and Beryllium Toxicity ResearchFrench-language works237,207