Time-limited Trials in the Intensive Care Unit to Promote Goal-Concordant Patient Care
Bibliographic record
Abstract
Consider the hypothetical case of a 75-year-old patient admitted to the intensive care unit (ICU) for acute hypoxic respiratory failure due to pneumonia and systolic heart failure. Although she suffers from a potentially treatable infection, her advanced age and chronic illness increase her risk of experiencing a poor outcome. Her family feels conflicted about whether the use of mechanical ventilation would be acceptable given what they understand about her values and preferences. In the ICU setting, clinicians, patients, and surrogate decision-makers frequently face challenges of prognostic uncertainty as well as uncertainty regarding patients' goals and values. Time-limited trials (TLTs) of life-sustaining treatments in the ICU have been proposed as one strategy to help facilitate goal-concordant care in the midst of a complex and high-stakes decision-making environment. TLTs represent an agreement between clinicians and patients or surrogate decision-makers to employ a therapy for an agreed-upon time period, with a plan for subsequent reassessment of the patient's progress according to previously-established criteria for improvement or decline. Herein, we review the concept of TLTs in intensive care, and explore their potential benefits, barriers, and challenges. Research demonstrates that, in practice, TLTs are conducted infrequently and often incompletely, and are challenged by system-level factors that diminish their effectiveness. The promise of TLTs in intensive care warrants continued research efforts, including implementation studies to improve adoption and fidelity, observational research to determine optimal timeframes for TLTs, and interventional trials to determine if TLTs ultimately improve the delivery of goal-concordant care in the ICU.
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 | 0.165 | 0.335 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| 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".