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Record W2793391328 · doi:10.1016/j.jalz.2017.12.006

The cost of Alzheimer's disease in China and re‐estimation of costs worldwide

2018· article· en· W2793391328 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAlzheimer s & Dementia · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Key Scientific Instrument and Equipment Development Projects of China
KeywordsChinaEconomic costSocioeconomic statusEstimationTotal costCost estimateBurden of diseaseDementiaEnvironmental healthSocioeconomicsDiseaseMedicineBusinessEconomicsGeographyPopulation

Abstract

fetched live from OpenAlex

INTRODUCTION: The socioeconomic costs of Alzheimer's disease (AD) in China and its impact on global economic burden remain uncertain. METHODS: We collected data from 3098 patients with AD in 81 representative centers across China and estimated AD costs for individual patient and total patients in China in 2015. Based on this data, we re-estimated the worldwide costs of AD. RESULTS: The annual socioeconomic cost per patient was US $19,144.36, and total costs were US $167.74 billion in 2015. The annual total costs are predicted to reach US $507.49 billion in 2030 and US $1.89 trillion in 2050. Based on our results, the global estimates of costs for dementia were US $957.56 billion in 2015, and will be US $2.54 trillion in 2030, and US $9.12 trillion in 2050, much more than the predictions by the World Alzheimer Report 2015. DISCUSSION: China bears a heavy burden of AD costs, which greatly change the estimates of AD cost worldwide.

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.

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)0.000
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 score0.428
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.321
Teacher spread0.301 · 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