The clinical application of traditional Chinese medicine NRICM101 in hospitalized patients with COVID-19
Bibliographic record
Abstract
Background The aim of this study was to assess the efficacy and safety of NRICM101 in hospitalized patients with COVID-19.Research design and methods We conducted a retrospective study from 20 April 2021 to 8 July 2021, and evaluated the safety and outcomes (mortality, hospital stay, mechanical ventilation, oxygen support, diarrhea, serum potassium) in COVID-19 patients. Propensity score matching at a 1:2 ratio was performed to reduce confounding factors.Results A total of 201 patients were analyzed. The experimental group (n = 67) received NRICM101 and standard care, while the control group (n = 134) received standard care alone. No significant differences were observed in mortality (10.4% vs. 14.2%), intubation (13.8% vs. 11%), time to intubation (10 vs. 11 days), mechanical ventilation days (0 vs. 9 days), or oxygen support duration (6 vs. 5 days). However, the experimental group had a shorter length of hospitalization (odds ratio = 0.12, p = 0.043) and fewer mechanical ventilation days (odds ratio = 0.068, p = 0.008) in initially severe cases, along with an increased diarrhea risk (p = 0.035).Conclusion NRICM101 did not reduce in-hospital mortality. However, it shortened the length of hospitalization and reduced mechanical ventilation days in initially severe cases. Further investigation is needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".