Can a Physiologic Insight “Resuscitate” Research in Cardiopulmonary Resuscitation?
Bibliographic record
Abstract
genetic and environmental factors that affect gene regulation affect disease risk, remains a major challenge.The discovery that a regulatory variant affecting MUC5B expression in distal airways is associated with a very large increase in risk of developing pulmonary fibrosis is a compelling early step toward this goal (12).It will also be critical to identify disease-associated changes in mucin gene expression in different regions of the lung and to understand how these affect mucus function.Recent studies show that differences in mucus composition are associated with dramatic differences in mucus organization and function.For example, MUC5B and MUC5AC are found within distinct domains of mucus plugs in fatal asthma, and the MUC5AC-rich domains play a unique role in mucostasis by tethering to the epithelium (13).In pigs, MUC5B from submucosal gland ducts formed strands composed of multiple MUC5B filaments, whereas MUC5AC emerged from superficial secretory cells as wispy threads or sheets, and it seems likely that these distinct structures contribute differently to mucociliary transport ( 14).Understanding how regional and disease-associated differences in mucins and other mucus components affect host defense and lung function is likely to be a long but rewarding journey.Okuda and colleagues have provided a map that will help us find our way.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.032 | 0.046 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".