Bibliographic record
Abstract
There is an increasing evidence that survivors of critical illness suffer from impaired health-related quality of life. There are relatively few studies that have evaluated the effect of interventions in clinical trials to improve these long-term outcomes. Studies to improve long-term outcomes after critical illness face many of the same challenges encountered in studies designed to improve shorter term outcomes in the intensive care unit. These include an incomplete understanding of the causal mechanisms involved in post-intensive care unit impairment, trouble in identifying patients ill enough to benefit from an intervention but whose impairment is not fixed, and identifying proper outcome variables. There are, however, unique challenges to clinical trials including bias from competing mortality and incomplete follow-up. 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 intensive care unit and after patients leave the hospital, for intensivists to target. Those interested in this area should collaborate to build on the lessons from effective multidisciplinary programs developed to treat other diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.491 | 0.728 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".