Role of Traditional Chinese Medicine in the Management of Viral Pneumonia
Bibliographic record
Abstract
Viral pneumonia is one kind of acute respiratory tract infection caused by the virus. There have been many outbreaks of viral pneumonia with high contagiousness and mortality both in China and abroad, such as the great influenza in 1918, the severe acute respiratory syndrome (SARS) coronavirus in 2003, the Influenza A (H1N1) virus in 2009, and the Middle East Respiratory Syndrome coronavirus (MERS-CoV) in 2012 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019. These outbreaks and/or pandemic have significant impact on human life, social behaviors, and economic development. Moreover, no specific drug has been developed for these viruses. Traditional Chinese medicine (TCM) plays an important role in the treatment of viral pneumonia during these outbreaks especially in SARS and SARS-CoV-2 because studies suggest that TCM formulations may target several aspects of the disease and may have lesser side effects than manufactured pharmaceuticals. In recent years, a lot of clinicians and researchers have made a series of in-depth explorations and investigations on the treatment of viral pneumonia with TCM, which have understood TCM therapeutic mechanisms more specifically and clearly. But critical analysis of this research in addition to further studies are needed to assess the potential of TCM in the treatment of viral pneumonia.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".