Experts’ Consensus on the Management of Respiratory Disease Syndemic
Bibliographic record
Abstract
The global burden of respiratory diseases is a significant and increasing threat to individuals worldwide.In 2017, there were 544.9 million cases of chronic respiratory diseases, a 39.8% increase since 1990 (1).These diseases were the third leading cause of global mortality in 2017, accounting for 7.0% of all deaths, an 18.0% increase compared to 1990.In addition to chronic respiratory diseases, acute infectious respiratory diseases, including influenza, coronavirus diseases 2019 (COVID-19), and respiratory syncytial virus, pose significant public health concerns and cause both short-term and longterm health damages (2-3).The presence of complex coexisting diseases in the respiratory system further complicates treatment and increases the burden of disease.To effectively address these challenges, it is crucial to implement a comprehensive and robust management approach.A syndemic refers to the co-occurrence of multiple diseases or health conditions within a population, where biological or behavioral factors worsen the negative health impacts of these conditions (4).Syndemic theory suggests that the combined presence of diseases, along with social and environmental factors, synergistically affects population health.This theory provides a valuable framework for understanding and addressing respiratory disease syndemics.Managing respiratory diseases from a syndemic perspective necessitates a deep understanding of the intricate interplay between biological, social, and environmental factors that contribute to the occurrence and progression of these diseases.By adopting a syndemic approach, the focus shifts from managing individual diseases to a collaborative model that prioritizes population-level interventions, including proactive diagnosis, comprehensive assessment of disease severity, and integrated management of conditions associated with respiratory diseases.The expert consensus on managing respiratory disease syndemics aims to support research and practical interventions in addressing these complex respiratory health challenges.
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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 | 0.025 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".