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Record W4210655334 · doi:10.21203/rs.3.rs-1292258/v1

The burden of injury in Central, Eastern, and Western European sub-region: a systematic analysis from the Global Burden of Disease 2019 Study

2022· preprint· en· W4210655334 on OpenAlexaff
Periklis Charalampous, Juanita A. Haagsma, Filippo Ariani, Anne Gallay, Kim Moesgaard Iburg, Evangelia Nena, Che Henry Ngwa, Alexander Rommel, Ausra Zelviene, Kedir Hussein Abegaz, Hanadi Al Hamad, Luciana Albano, Cătălina Liliana Andrei, Tudorel Andrei, Ippazio Cosimo Antonazzo, Olatunde Aremu, Ashokan Arumugam, Alok Atreya, Avinash Aujayeb, José Luís Ayuso‐Mateos, Luchuo Engelbert Bain, Maciej Banach, Till Bärnighausen, Francesco Barone‐Adesi, Massimiliano Beghi, Derrick Bennett, Akshaya Srikanth Bhagavathula, Félix Carvalho, Giulio Castelpietra, Joht Singh Chandan, Rosa A S Couto, Natália Martins, Giovanni Damiani, Άννα Δαστιρίδου, Andreas K. Demetriades, Diana Dias da Silva, Adeniyi Francis Fagbamigbe, Seyed‐Mohammad Fereshtehnejad, Eduarda Fernandes, Pietro Ferrara, Florian Fischer, Urbano Fra Paleo, Silvia Ghirini, James Glasbey, Ionela-Roxana Glăvan, Nelson G. M. Gomes, Michal Grivna, Netanja I. Harlianto, Josep María Haro, M. Tasdik Hasan, Sorin Hostiuc, Ivo Iavicoli, Milena Ilić, Irena Ilić, Mihajlo Jakovljević, Jost B. Jonas, Jacek Jerzy Jozwiak, Mikk Jürisson, Joonas H. Kauppila, Gbenga A Kayode, Moien AB Khan, Adnan Kısa, Sezer Kısa, Ai Koyanagi, Manasi Kumar, Om Kurmi, Carlo La Vecchia, Demetris Lamnisos, Savita Lasrado, Paolo Lauriola, Shai Linn, Joana A. Loureiro, Raimundas Lunevičius, Áurea Madureira-Carvalho, Enkeleint A. Mechili, Azeem Majeed, Ritesh G. Menezes, Alexios‐Fotios A. Mentis, Atte Meretoja, Tomislav Meštrović, Tomasz Miazgowski, Bartosz Miazgowski, Andreea Mirică, Mariam Molokhia, Shafiu Mohammed, Lorenzo Monasta, Francesk Mulita, Mukhammad David Naimzada, Ionuţ Negoi, Subas Neupane, Bogdan Oancea, Hans Orru, Adrian Oţoiu, Nikita Otstavnov, Stanislav S Otstavnov, Alicia Padrón‐Monedero, Songhomitra Panda‐Jonas, Shahina Pardhan, Jay Patel, Paolo Pedersini, Marina Pinheiro, Ivo Rakovac, Chythra R Rao, Salman Rawaf, David Laith Rawaf, Violet Rodrigues, Luca Ronfani, Dominic Sagoe, Francesco Sanmarchi, Milena M Santric-Milicevic, Brijesh Sathian, Aziz Sheikh, Rahman Shiri, Siddharudha Shivalli, Inga Dóra Sigfúsdóttir, Rannveig Sigurvinsdóttir, Valentin Yurievich Skryabin, Anna Aleksandrovna Skryabina, Catalin-Gabriel Smarandache, Bogdan Socea, Raúl A. R. C. Sousa, Paschalis Steiropoulos, Rafael Tabarés‐Seisdedos, Marcos Roberto Tovani‐Palone, Fimka Tozija, Sarah Van de Velde, Tommi Vasankari, Massimiliano Veroux, Francesco Saverio Violante, Vasily Vlassov, Yanzhong Wang, Ali Yadollahpour, Sanni Yaya, Михаил Сергеевич Застрожин, Anasthasia Zastrozhina, Suzanne Polinder, Marek Majdán

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of Ottawa
FundersBill and Melinda Gates Foundation
KeywordsBurden of diseaseDiseaseGeographyPolitical scienceMedicinePathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.007
Bibliometrics0.0050.010
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.082
GPT teacher head0.395
Teacher spread0.313 · 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 designMeta-analysis
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
Published2022
Admission routes1
Has abstractno

Explore more

Same venueResearch Square→Same topicClimate Change and Health Impacts→French-language works237,207→