Doenças Pulmonares Crônicas em Crianças até 12 Anos de Idade e Suas Implicações Fisiológicas e Clínicas em Voos Comerciais
Bibliographic record
Abstract
Flying became such a convenient way of travelling because it is easy, quick and safe. According to the Canadian Pediatric Society, each year 1 billion people fly anywhere around the world and it has been reported that the number of passengers under 12 years of age increased in airplane trips. The exposure of healthy children and others with respiratory conditions to low partial pressure ambient increased in the last 20 years. The risk of hypoxia is greater in this population because of its specific anatomy and physiology that can lead to possible ventilationperfusion mismatch, vasoconstriction and bronchospasm. This article consists of a review of the literature and it aims to analyze the physiological and clinical implications related to commercial flights of chronic lung diseases in children under 12 years of age; to identify which of them may restrain or contraindicate this activity and to discuss the use of oxygen in-flight. We conclude that there are few absolute contraindications. However, current studies portray no scientific evidence. So, a rigorous fly assessment is necessary to promote security. Pediatricians are therefore responsible for an adequate clinical history and recommendations.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".