Pediatric Interprofessional ICU Ethics Rounds: A Single-Center Study
Bibliographic record
Abstract
OBJECTIVES: We sought to examine whether sociodemographic differences, such as race and socioeconomic status, existed between patients in the PICU, pediatric cardiothoracic ICU (PCTU), and NICU who were identified as having ethical issues during interprofessional ethics rounds and all other patients admitted to these units and to characterize the primary ethical issues identified in this context. METHODS: We compared sociodemographic factors among patients admitted to a quaternary academic children’s hospital between January 2017 and December 2018 who were identified as having ethical issues during PICU, PCTU, and NICU interprofessional ethics rounds (n = 122) with those of all other patients admitted to these units (n = 4971). χ2 tests or Fisher’s exact tests, Mann–Whitney U tests, and a multivariable logistic regression analysis were performed. RESULTS: With bivariate analyses, we detected significant differences by race, insurance type, and ventilator dependence, but no significant differences between the 2 groups existed on the basis of sex, ethnicity, religion, primary language, age, or a socioeconomic status metric. After we adjusted for confounders using a multivariable logistic regression analysis, only patients who were ventilator dependent were at significantly higher odds (odds ratio = 5.78; confidence interval = 3.69–9.04; P < .001) of being identified as having ethical issues. Goals of care was the most frequent ethical issue (44%). CONCLUSIONS: Except for ventilator dependence, patients with ethical issues during PICU, PCTU, and NICU interprofessional ethics rounds are demographically similar to overall patients admitted in these units. Future research should be used to assess whether proactive rounds impact the timing of ethics consultation requests as well as to determine if interprofessional ethics rounds influence volume and acuity in formal ethics consultation practices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".