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Record W2004806474 · doi:10.7895/ijadr.v2i1.132

The comparative risk assessment for alcohol as part of the Global Burden of Disease 2010 Study: What changed from the last study?

2013· article· en· W2004806474 on OpenAlexaffvenue
Jürgen Rehm, Guilherme Borges, Gerhard Gmel, Kathryn Graham, Bridget F. Grant, Charles Parry, Vladimir Poznyak, Robin Room

Bibliographic record

VenueThe International Journal of Alcohol and Drug Research · 2013
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsAlcohol consumptionBurden of diseaseDiseasePopulationRisk factorMedicineGerontologyDemographyEnvironmental healthAlcoholSociologyInternal medicine

Abstract

fetched live from OpenAlex

Rehm, J., Borges, G., Gmel, G., Graham, K., Grant, B., Parry, C., Poznyak, V. & Room R. (2013). The comparative risk assessment for alcohol as part of the Global Burden of Disease 2010 study: What changed from the last study? International Journal of Alcohol and Drug Research, 2(1), 1-5. doi: 10.7895/ijadr.v2i1.132 (http://dx.doi.org/10.7895/ijadr.v2i1.132)In December 2012, the new results of the Comparative Risk Assessment (CRA) for alcohol within the Global Burden of Disease and Injury (GBD) Study 2010 were presented at a joint meeting of the GBD Group and the journal Lancet at the Royal Society in London (Lim et al., 2012). At first glance, there do not appear to be many changes to alcohol consumption as a risk factor for death and disability: it is identified as the third most important risk factor, as it was in the last CRA (World Health Organization, 2009). The burden of disease attributable to alcohol had increased, compared to the 2004 estimate (Rehm, Mathers et al., 2009), but this could be due to an increase in global population, or to variations in the methodologies behind the 2004 and 2010 estimates.

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)0.002
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 score0.109
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.119
GPT teacher head0.447
Teacher spread0.328 · 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

Citations18
Published2013
Admission routes2
Has abstractyes

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