Quantifying the Association Between Code Status Discussions and Outcomes in Critically Ill Older Adults Admitted to the Intensive Care Unit (ICU): A Retrospective Cohort Study
Bibliographic record
Abstract
Background: “Do Not Resuscitate” (DNR) status has been implicated as an independent risk factor for mortality in patients admitted to the ICU. The implications of DNR status in older, critically ill patients for whom these conversations are often most relevant are less known. Objective: To determine the relationship between code status and mortality in a subset of critically ill, older ICU patients. Methods: Retrospective cohort study of critically ill older adults as defined by an APACHE II score ≥20 and age ≥70, admitted to the ICU at a large community hospital in Ontario from 1 January 2013 to 31 December 2018. Results: Of 613 patients admitted to the ICU, 163 met the inclusion criteria. Of these, 64 (39.3%) had a DNR order, while the remaining 99 (60.7%) did not and were considered full code. We found a strong association between DNR status and mortality (OR 2.61; 95% CI 1.33 to 5.09). Patients with a DNR order stayed fewer days in the ICU (7.7 days (±3.6) vs. 9.9 days (±8.3)) and used fewer resources than similarly ill patients who were full code with no difference in discharge morbidity. Patients with a DNR order had lower average costs of hospital and ICU admissions in comparison to patients who were full code (CAD 49,589.10/pt. vs. CAD 59,704.70/pt. (Canadian dollars)). Conclusions: Among critically ill, older ICU patients, DNR status is strongly associated with in-hospital mortality. Those in the full code group used more resources, resulting in higher costs of hospitalization without any difference in discharge morbidity.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".