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The global burden of injury: incidence, mortality, disability-adjusted life years and time trends from the Global Burden of Disease study 2013

2015· article· en· W2221016424 on OpenAlexaff
Juanita A. Haagsma, Nicholas Graetz, Ian Bolliger, Mohsen Naghavi, Hideki Higashi, Erin C Mullany, Semaw Ferede Abera, Jerry Abraham, Adofo Koranteng, Ubai Alsharif, Emmanuel A Ameh, Walid Ammar, Carl Abelardo T. Antonio, Lope H. Barrero, Tolesa Bekele, Dipan Bose, Alexandra Bražinová, Ferrán Catalá-López, Lalit Dandona, Rakhi Dandona, Paul I. Dargan, Diego De Leo, Louisa Degenhardt, Sarah Derrett, Samath Dhamminda Dharmaratne, Tim Driscoll, Leilei Duan, Ermakov Sp, Farshad Farzadfar, Valery L. Feigin, Richard C. Franklin, Belinda J. Gabbe, Richard A. Gosselin, Nima Hafezi‐Nejad, Randah R Hamadeh, Martha Hı́jar, Guoqing Hu, Sudha Jayaraman, Guohong Jiang, Yousef Khader, Ejaz Ahmad Khan, Sanjay Krishnaswami, Chanda Kulkarni, Fiona Lecky, Ricky Leung, Raimundas Lunevičius, Ronan A Lyons, Marek Majdán, Amanda J. Mason‐Jones, Richard Matzopoulos, Peter A. Meaney, Ted R. Miller, Charles Mock, Rosana Norman, Ricardo Orozco, Suzanne Polinder, Farshad Pourmalek, Vafa Rahimi‐Movaghar, Amany Refaat, David Rojas‐Rueda, Nobhojit Roy, David C. Schwebel, Amira Shaheen, Saeid Shahraz, Vegard Skirbekk, Kjetil Søreide, Sergey Soshnikov, Dan J. Stein, Bryan L. Sykes, Karen M. Tabb, Awoke Misganaw Temesgen, Eric Y. Tenkorang, Alice Theadom, Bach Xuan Tran, Tommi Vasankari, Monica S. Vavilala, Vasily Vlassov, Solomon Woldeyohannes, Paul Yip, Naohiro Yonemoto, Mustafa Z Younis, Chuanhua Yu, Christopher Murray, Theo Vos, Shivanthi Balalla, Michael Phillips

Bibliographic record

VenueInjury Prevention · 2015
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsMemorial University of NewfoundlandUniversity of British Columbia
FundersMedical Research CouncilBill and Melinda Gates Foundation
KeywordsBurden of diseaseDisease burdenIncidence (geometry)Injury preventionPoison controlMedicineOccupational safety and healthHuman factors and ergonomicsSuicide preventionDiseaseYears of potential life lostEnvironmental healthGerontologyLife expectancyInternal medicinePopulationPathology

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.012
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.003

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 head0.037
GPT teacher head0.370
Teacher spread0.333 · 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 source (direct Gemma or distilled Codex), 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

Citations1,330
Published2015
Admission routes1
Has abstractyes

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