Goals of care discussions among critically Ill patients on vasopressor treatment
Bibliographic record
Abstract
BACKGROUND: Goals of care (GOC) discussions are essential for aligning medical management with the values of critically ill patients, particularly those requiring vasopressors, such as dopamine. To evaluate GOC discussions in terms of prevalence, factors associated with documentation, and impact on survival among critically ill patients requiring vasopressors for hemodynamic support. METHODS: We conducted a retrospective cohort study at a tertiary healthcare facility in Riyadh, Saudi Arabia, focusing on patients admitted to intensive care units (ICUs) and internal medicine (IM) wards. The study included adult in patients who received dopamine during their hospital stay. Factors associated with GOC documentation were identified using logistic regression analysis. The 30-day and 1-year survival rates according to GOC discussions were analyzed using Kaplan-Meier survival curves, which were compared using the log-rank test. RESULTS: Of 301 patients, 56.8% were men and 64.8% were aged ≥60 years. GOC discussions were documented in 61.8% of patients and were more frequent among older patients (≥60 years) than among younger patients (73.1% vs. 51.3%, p < 0.001) and in those with higher APACHE II scores (median 21.0 vs. 18.0, p = 0.001). Multivariable analysis identified age ≥ 60 years as independent precipitating factor of GOC discussions (odds ratio 2.41, 95% confidence interval 1.34-4.32, p = 0.003). The study found significantly lower survival rates at both 30 days and 1 year among patients who had documented GOC discussions. CONCLUSIONS: GOCs were more prevalent among critically ill older patients. The study found significantly higher mortality rates at both 30 days and 1 year among patients who had documented GOC discussions. These findings highlight the need for institutional strategies to integrate GOC discussions into routine care and address their potential implications on patient outcomes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".