A new era for ICU-acquired weakness research—from mechanisms to meaningful recovery
Bibliographic record
Abstract
Intensive care unit-acquired weakness (ICUAW) remains one of the most persistent and disabling sequelae of critical illness.Despite decades of research and a growing emphasis on survivorship, our understanding of its mechanisms, prevention, and management remains incomplete.In a recent issue of Intensive Care Medicine, Eggmann and colleagues [1] present a landmark multinational and interprofessional research agenda for ICUAW.This initiative brings together 51 experts -including clinicians, scientists, and individuals with lived experienceto define ten priority questions that will shape the next decade of research. From pathophysiology to personalisationBuilding on the 2017 ICM research roadmap by Latronico et al. [2], this new agenda decisively broadens the scope.The focus shifts from describing mechanisms to harnessing them for individualized therapy.The authors highlight that early muscle loss-averaging 15% in the first ICU week-reflects complex interactions between systemic inflammation, bioenergetic failure, and impaired protein synthesis rather than immobility alone.Accordingly, future research must integrate mechanistic biomarkers such as urea-to-creatinine ratio, creatinine production rate, and urinary titin into clinical studies, linking biology to the bedside.
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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 | 0.039 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.013 | 0.033 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.014 | 0.027 |
| Insufficient payload (model declined to judge) | 0.010 | 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".