Defining and Characterizing Inappropriate Goals of Care Designation: A 10-year Retrospective Multicenter ICU Cohort Study
Bibliographic record
Abstract
BACKGROUND: Goals of Care Designation (GCD) is a medical order used to describe and communicate the general aim of care. It includes a "code status" that guides the healthcare team on which interventions to offer during acute clinical deterioration. Inappropriate GCD occurs when there is lack of communication on the patient's wishes and values in the context of their health status, documentation of the conversation and plan, or agreement on medical effectiveness between patient, family and health care team stakeholders. The frequency of inappropriate GCD, the contributing factors, and their outcomes in ICUs are unknown. METHODS: Using an existing quality assurance database, we conducted a retrospective multicenter cohort study of adult patients who died in the ICU between January 1, 2010 and December 31, 2019 to determine the frequency, etiology, and associated stakeholder and contextual features of patients flagged with goals of care concern by physician reviewers. RESULTS: Of 4656 patients who died in the ICU and underwent a standardized morbidity and mortality review, 265 cases (5.7%) met criteria for inappropriate GCD for further analysis. Cases had one or more elements of suboptimal communication (n = 119, 44.9%), documentation practices (n = 77, 29.1%), or agreement of stakeholders (n = 115, 43.4%). Escalation in GCD to more intensive resuscitation orders occurred in 57 cases (21.5%) with common contextual features of crisis communication in the ER, or in preparation for a surgery or procedure. CONCLUSION: We validated one definition of inappropriate GCD through a large retrospective cohort that can be used as a baseline incidence for future QI endeavors. Through this cohort analysis, a breadth of system opportunities to reduce inappropriate care through optimization of communication, documentation, and stakeholder decision-making processes is described.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".