Greater recovery after critical illness (GRACE): a call to action to create a new roadmap for critical illness research
Bibliographic record
Abstract
For decades, most critical care patients have survived hospitalisation, supporting increased attention on the long-term critical illness recovery. The term 'Post-Intensive Care Syndrome' was coined in 2012 to raise awareness of long-term impairment in physical, cognitive and/or mental health after critical illness. However, the incidence of these impairments has persisted over the past decade, reaching as high as 60% and remains a major public health problem.Aiming to set a research agenda to address evidence gaps in critical illness recovery over the next 10 years, we invited key international opinion leaders from diverse clinical and methodological backgrounds to a roundtable meeting in June 2024 to assess the progress of post-critical illness recovery research and outline a future research agenda to address the unmet needs of critical illness survivors over the next decade.An early outcome from the meeting was to conduct a thematic analysis of critical care recovery literature, which highlighted the need for effective expectation management, ongoing patient support and education throughout recovery, integration between inpatient and community care, caregiver support and opportunities to reconnect with the intensive care unit.Participants identified conceptual challenges concerning current terminology and scope, population heterogeneity and phenotyping, and outcome definitions. Methodological challenges were identified around study design, with a call to shift to contemporary trial designs, incorporating qualitative methods. Translation into clinical practice will require interdisciplinary engagement.The roundtable concluded that a roadmap should be developed to guide clinical and research efforts over the coming decade, with the aim of developing a precision recovery approach.
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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 | 0.194 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.030 | 0.062 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.036 | 0.061 |
| Insufficient payload (model declined to judge) | 0.024 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".