Dissecting cardiovascular disease-associated noncoding genetic variants using human iPSC models
Bibliographic record
Abstract
Timely adjustments of antibiotic and corticosteroid treatments are vital for patients with diffuse parenchymal lung diseases (DPLDs). In this study, 41 DPLD patients with negative metagenomic next-generation sequencing (mNGS) results who were responsive to corticosteroids were enrolled. Among these patients, about 26.8% suffered from drug-induced DPLD, while 9.8% presented autoimmune-related DPLD. Following the report of the negative mNGS results, in 34 patients with complete antibiotics administration profiles, 79.4% (27/34) patients discontinued antibiotics after receiving negative mNGS results. Moreover, 70.7% (29/41) patients began or increased the administration of corticosteroid upon receipt of negative mNGS results. In the microbiota analysis, Staphylococcus and Stenotrophomonas showed higher detection rates in patients with oxygenation index (OI) below 300, while Escherichia and Stenotrophomonas had higher abundance in patients with pleural effusion. In summary, our findings demonstrated the clinical significance of mNGS in assisting the antibiotic and corticosteroid treatment adjustments in corticosteroid-responsive DPLD. Lung microbiota may imply the severity of the disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".