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Record W7073083470

�쓬二쇰줈 �씤�븳 �궗�쉶寃쎌젣�쟻 鍮꾩슜

2015· article· en· W7073083470 on OpenAlexaboutno aff

Bibliographic record

VenueYUHSpace (Yonsei University Medical Library) · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionDiafiltrationArticular cartilage damagePretextHyporeflexiaTSG101
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to estimate socioeconomic costs caused by alcohol drinking in Korea as of 2004 in an effort to raise the awareness of the gravity of problems associated with alcohol drinking and the necessity of active intervention by family physicians. METHODS: The costs were classified as direct costs, indirect costs and other costs. The direct costs consisted of direct medical costs and direct non-medical costs. The indirect costs were computed by the reduction and loss of productivity and the loss of workforce. Other costs consisted of property loss, administration costs and costs of alcohol beverage. RESULTS: The annual costs, which seemed to be attributable to alcohol drinking, were estimated to be 200,990 hundred million won (2.9% of GDP). In the case of the former, the amount included 38.83% for reduction of productivity, 26.92% for loss of the workforce, 22.24% for alcoholic beverage, 5.34% for direct medical costs, 2.29% for loss of productivity, 1.87% for direct non- medical costs, 1.54% for administration costs and 0.97% for loss of property. CONCLUSION: Our study confirms that compared with the cases of Japan (1.9% of GNP), Canada (1.09% of GDP), France (1.42% of GDP) and Scotland (1.19% of GDP), alcohol drinking incurs substantial socioeconomic costs to Koreans. An active intervention by family physicians is suggested.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.217
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.005

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 head0.394
Teacher spread0.321 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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