Multiple-organ dysfunction syndrome in China
Bibliographic record
Abstract
In this article we review the current status of multiple-organ dysfunction syndrome (MODS) in China, with a focus on its epidemiology and pathogenesis and clinical treatment approaches. Current and future research directions are also reviewed with the overall goal of improving our understanding of this disorder so that optimal prevention and treatment strategies can be developed. Pathogenically, Chinese scholars hypothesize that MODS occurs in accordance with a “two-hit” model, which results in an overwhelming systemic inflammatory response within the host. Lipopolysaccharide or endotoxin activates several intracellular signaling pathways via cellular membrane receptors, thereby contributing to this inflammatory response. Supportive therapy of vital organs represents the main therapeutic approach for these patients, although nutritional support in the form of early enteral feeding is also important. Additional approaches include the administration of agents to increase the rate of protein synthesis and modulate the host's immune response. Herbal medicines, together with other traditional Chinese medicines, have also been shown to be effective. With the application of these various therapeutic measures, great progress has been made in the prevention and treatment of MODS in the last 15 years. However, since the 1990s, the mortality due to MODS in China has remained unacceptably high, with little prospect of improvement. Future efforts should focus on more scientific research in this area, with the overall goal of reducing the morbidity and mortality of MODS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".