Evaluating long-term outcome in survivors of critical illness: “Seeing is believing”–a case for ambulatory follow-up
Bibliographic record
Abstract
The goal of devising a severity of illness scoring system that reflects long-term outcomes in survivors of critical illness is a laudable one. However, several obstacles must be overcome before it can be achieved. We need to determine which morbid outcomes are most informative in critically ill populations, how best to measure these outcomes, and how they influence the subsequent pattern and cost of health care use. This review suggests a reevaluation of our approach to outcome studies in survivors of critical illness. We need to abandon the traditional, compartmentalized view of critical illness as an intensive care unit-centered phenomenon. Instead, we need to adopt the concept of a continuum from premorbid disease to critical illness and ultimately to a debilitated patient in need of physical and psychological rehabilitation. This longitudinal model for outcomes research in critical care can be achieved only through rigorous ambulatory follow-up of survivors of critical illness. This detailed follow-up data may facilitate the development of predictors that might prove valuable in future scoring system models.
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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 | 0.027 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".