Novel approaches to metabolic assessment and structured exercise to promote recovery in ICU survivors
Bibliographic record
Abstract
PURPOSE OF REVIEW: Survivorship or addressing impaired quality of life (QoL) in ICU survivors has been named 'the defining challenge of critical care' for this century to address this challenge; in addition to optimal nutrition, we must learn to employ targeted metabolic/muscle assessment techniques and utilize structured, progressive ICU rehabilitative strategies. RECENT FINDINGS: Objective measurement tools such as ccardiopulmonary exercise testing (CPET) and muscle-specific ultrasound show great promise to assess/treat post-ICU physical dysfunction. CPET is showing that systemic mitochondrial dysfunction may underlie development and persistence of poor post-ICU functional recovery. Finally, recent data indicate that we are poor at delivering effective, early ICU rehabilitation and that there is limited benefit of currently employed later ICU rehabilitation on ICU-acquired weakness and QoL outcomes. SUMMARY: The combination of nutrition with effective, early rehabilitation is highly likely to be essential to optimize muscle mass/strength and physical function in ICU survivors. Currently, technologies such as muscle-specific ultrasound and CPET testing show great promise to guide ICU muscle/functional recovery. Further, we must evolve improved ICU-rehabilitation strategies, as current methods are not consistently improving outcomes. In conclusion, we must continue to look to other areas of medicine and to athletes if we hope to ultimately improve 'ICU Survivorship'.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".