Remote ischemic conditioning for acute respiratory distress syndrome in COVID-19
Bibliographic record
Abstract
Acute respiratory distress syndrome and subsequent respiratory failure remains the leading cause of death (>80%) in patients severely impacted by COVID-19. The lack of clinically effective therapies for COVID-19 calls for the consideration of novel adjunct therapeutic approaches. Though novel antiviral treatments and vaccination hold promise in control and prevention of early disease, it is noteworthy that in severe cases of COVID-19, addressing "run-away" inflammatory cascades are likely more relevant for improvement of clinical outcomes. Viral loads may decrease in severe, end-stage coronavirus cases, but a systemically damaging cytokine storm persists and mediates multiple organ injury. Remote ischemic conditioning (RIC) of the limbs has shown potential in recent years to protect the lungs and other organs against pathological conditions similar to that observed in COVID-19. We review the efficacy of RIC in protecting the lungs against acute injury and current points of consideration. The beneficial effects of RIC on lung injury along with other related cardiovascular complications are discussed, as are the limitations presented by sex and aging. This adjunct therapy is highly feasible, noninvasive, and proven to be safe in clinical conditions. If proven effective in clinical trials for acute respiratory distress syndrome and COVID-19, application in the clinical setting could be immediately implemented to improve outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".