Interventions to improve long-term outcomes after critical illness
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this article is to review the literature specifically on interventions that are targeted at improving health related quality of life in survivors of critical illness. Although there is a growing literature describing the impairment in quality of life of survivors of critical illness, there are a considerably smaller number of studies describing effective interventions at treating or preventing these complications. The topic is complex because critical illness spans a number of diseases including spinal cord injury and myocardial infarction that have an extensive rehabilitation literature. RECENT FINDINGS: To date there are limited studies to guide clinicians in treatments to prevent or treat the sequelae of critical illness. Standard therapies such as daily interruption of sedation and lung protective ventilation do not appear to worsen long-term outcomes. Insulin therapy and stress dose corticosteroids may be beneficial in preventing neuromuscular complications and posttraumatic stress disorder, respectively. A self-help manual for survivors appears to improve physical functioning. SUMMARY: Research interest in developing interventions to improve long-term outcome after critical illness is in its infancy and it is too early to make strong clinical recommendations. Multiple potential treatment areas exist both within the ICU and after patients leave the hospital for intensivists to target.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".