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Record W2015668451 · doi:10.1155/2015/510386

The Epidemiology and Economic Burden of<i>Clostridium difficile</i>Infection in Korea

2015· article· en· W2015668451 on OpenAlexfundno aff
Hyung-Yun Choi, So Youn Park, Young Ae Kim, Tai‐Young Yoon, Joong-Myung Choi, Bong‐Keun Choe, So-Hee Ahn, Seok‐Jun Yoon, Ye‐Rin Lee, In‐Hwan Oh

Bibliographic record

VenueBioMed Research International · 2015
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsnot available
FundersMinistry of Health, British Columbia
KeywordsClostridium difficileMedicineEpidemiologyEnvironmental healthInfection controlAntibioticsIntensive care medicineInternal medicineBiologyMicrobiology

Abstract

fetched live from OpenAlex

The prevalence of Clostridium difficile infection and the associated burden have recently increased in many countries. While the main risk factors for C. difficile infection include old age and antibiotic use, the prevalence of this infection is increasing in low-risk groups. These trends highlight the need for research on C. difficile infection. This study pointed out the prevalence and economic burden of C. difficile infection and uses the representative national data which is primarily from the database of the Korean Health Insurance Review and Assessment Service, for 2008-2011. The annual economic cost was measured using a prevalence approach, which sums the costs incurred to treat C. difficile infection. C. difficile infection prevalence was estimated to have increased from 1.43 per 100,000 in 2008 to 5.06 per 100,000 in 2011. Moreover, mortality increased from 69 cases in 2008 to 172 in 2011. The economic cost increased concurrently, from $2.4 million in 2008 to $7.6 million, $10.5 million, and $15.8 million in 2009, 2010, and 2011, respectively. The increasing economic burden of C. difficile infection over the course of the study period emphasizes the need for intervention to minimize the burden of a preventable illness like C. difficile infection.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.139
GPT teacher head0.425
Teacher spread0.286 · 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 designNot applicable
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

Citations39
Published2015
Admission routes1
Has abstractyes

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