MétaCan
Menu
Back to cohort
Record W4392845393 · doi:10.1590/scielopreprints.8269

PODEM A TROPONINA SANGUÍNEA E SALIVAR SER SINALISADORAS PRECOCES DO INFARTO AGUDO DO MIOCÁRDIO?

2024· preprint· pt· W4392845393 on OpenAlexaff
Flávia Kubrusly Arsego, Luiz Fernando Kubrusly, Douglas Mesadri Gewehr, Rafael Dib Possiedi, Paulo Afonso Nunes Nassif, Fernando Issamu Tabushi, Fernando Bermudez Kubrusly, Maria Luiza Ronkoski, Priscila Panassolo Cioato

Bibliographic record

Venuenot available
Typepreprint
Languagept
FieldComputer Science
TopicHealthcare during COVID-19 Pandemic
Canadian institutionsSunnybrook Hospital
Fundersnot available
KeywordsCardiologyMedicine

Abstract

fetched live from OpenAlex

Introdução: A lesão miocárdica pode ser identificada por biomarcadores extraídos das células cardíacas. A troponina é uma delas e pode ser observada no sangue e saliva. A saliva é preferencial pela facilidade de coleta não invasiva e pré-hospitalar. Objetivo: Revisar se a expressão de troponina I do fluído salivar no infarto agudo do miocárdio em triagens de emergências é viável comparado aos seus níveis plasmáticos. Método: A revisão da literatura foi feita colhendo informações publicadas no SciELO, Bibliomed, BVS - Biblioteca Virtual em Saúde, Pubmed e Scopus em português e inglês. A busca foi baseada em descritores relacionados ao tema, identificados como: “troponina, infarto agudo do miocárdio, biomarcadores cardíacos, fluido salivar”, com busca AND e OR. Resultados: Foram incluídos 109 trabalhos. Conclusão: O diagnóstico baseado na saliva oferece muitas opções podendo vir a ser opção muito interessante para elucidação de infarto agudo do miocárdio para o clínico geral, lares de idosos e serviços de transporte com rapidez auxiliando precocidade no tratamento.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Open science, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0050.001
Open science0.0070.013
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0020.011

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 head0.035
GPT teacher head0.334
Teacher spread0.299 · 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
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicHealthcare during COVID-19 PandemicFrench-language works237,207