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Record W2140532436 · doi:10.1186/1749-7922-7-s1-s13

The scientific production in trauma of an emerging country

2012· article· en· W2140532436 on OpenAlexaff
Gustavo Pereira Fraga, Vitor Augusto de Andrade, Ricardo Schwingel, Jamil Miguel Neto, Sizenando Vieira Starling, Sandro Rizoli

Bibliographic record

VenueWorld Journal of Emergency Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineProduction (economics)Medical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: The study aims to examine whether the end of specialty in trauma surgery in 2003 influenced the scientific productivity of the area in Brazil. METHODS: We identified and classified the manuscripts and their authors, from databases such as PubMed, Scielo and Plataforma Lattes and sites like Google, in addition to the list of members of SBAIT, the sole society in Brazil to congregate surgeons involved in trauma care in the country. We applied statistical tests to compare the periods of 1997-2003 and 2004-2010. We also analyzed the following variables: impact factor of journals in which manuscripts were published, journals, regional origin of authors, time since graduation, and conducting post-doctorate abroad. RESULTS: We observed a significant increase in publication rates of the analyzed groups over the years. There was a predominance of quantitative studies from the Southeast (especially the state of São Paulo). More time elapsed after graduation and the realization of postdoctoral studies abroad influenced the individual scientific productivity. CONCLUSION: The number of articles published by authors from the area of trauma has been growing over the past 14 years in Brazil. The end of the specialty in trauma surgery in the country did not influence the scientific productivity in the area.

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)0.007
metaresearch head score (Gemma)0.001
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 score0.116
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.050
GPT teacher head0.341
Teacher spread0.292 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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