Mapping the process of ICU care delivery to improve treatment decisions in acute respiratory failure
Bibliographic record
Abstract
Evidence suggests system-level norms and care processes influence individual patients' medical decisions, including end-of-life decisions for patients with critical illnesses like acute respiratory failure. Yet, little is known about how these processes unfold over the course of a patient's critical illness in the intensive care unit (ICU). Our objective was to map current-state ICU care delivery processes for patients with acute respiratory failure and to identify opportunities to improve the process. We conducted a process mapping study at two academic medical centers, using focus groups and semi-structured interviews. The 70 participants represented 17 distinct roles in ICU care, including interprofessional medical ICU and palliative care clinicians, surrogate decision makers, and patient survivors. Participants refined and endorsed a process map of current-state care delivery for all patients admitted to the ICU with acute respiratory failure requiring mechanical ventilation. The process contains four critical periods for active deliberation about the use of life-sustaining treatments. However, active deliberation steps are inconsistently performed and frequently disrupted, leading to prolongation of life-sustaining treatment by default, without consideration of patients' individual goals and priorities. Interventions to standardize active deliberation in the ICU may improve treatment decisions for ICU patients with acute respiratory failure.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".