Categorização dos pontos estratégicos da fisiologia de voo para o transporte aeromédico
Bibliographic record
Abstract
Objetivo: Categorizar os pontos estratégicos da fisiologia de voo que possam interferir no transporte aeromédico. Método: Trata-se de um estudo de revisão integrativa de literatura, realizada com base no modelo PRISMA - Preferred Reporting ltems for Systematic Reviews and Meta-Analyses. A busca dos artigos foi realizada nos meses de agosto e setembro de 2021 . Resultado: Foram utilizados 1 O trabalhos, elencadas seis categorias: (i) Altitude; (ii) Áreas comuns que precisam de atenção; (iii) Forças de Aceleração; (iv) Hipóxia, (v) Preparação para o voo do paciente; (vi) Umidade, Temperatura e Gravidade. Conclusão: O transporte em aeronaves de asa fixa necessita de um conhecimento de fisiologia de voo, potenciais alterações na altitude, recomendações específicas, equipe de saúde e tripulação capacitadas para reconhecer e intervir. Assim como, possuam práticas avançadas, compartilhem as informações, maximizem os processos de segurança e qualidade no ambiente hipobárico.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".