Differences in goals of care discussion outcomes among healthcare professionals: an observational cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Goals of care discussions ensure patients receive the care that they want. Recent studies have recognized the opportunity for allied health professionals, such as nurses, in facilitating goals of care discussions. However, the outcomes of such interventions are not well studied. AIM: To compare the outcomes of goals of care discussions led by physicians and nurses. DESIGN: This is a retrospective cohort study of patients admitted to an Internal Medicine unit from January 2018 to August 2019. A comprehensive chart review was performed on a random sample of patients. Patient's decision to accept or refuse cardiopulmonary resuscitation was recorded and analyzed. Analysis was stratified by patients' comorbidity burden and illness severity. SETTING/PARTICIPANTS: The study took place at a tertiary care center and included 200 patients. Patients aged ⩾ 18 were included. Patients who have had pre-existing goals of care documentation were excluded. RESULTS: = 0.003). Multiple regression showed that patients with higher comorbidity burden (OR 0.71, 95% CI: 0.62-0.82), more severe illness (OR 0.89, 95% CI 0.88-0.99), and physician-led goals of care discussions (OR 0.30, 95% CI: 0.15-0.62) were less likely to accept cardiopulmonary resuscitation. CONCLUSIONS: There was a significant difference between the outcomes of goals of care discussions led by nurses and physicians. Patients were more likely to accept aggressive resuscitative measures in nurse-led goals of care discussions. Further research efforts are needed to identify the factors contributing to this discrepancy, and to devise ways of improving goals of care discussion delivery.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".