Assessment of shared decision-making tool for eliciting informed goals of care in the hospitalized elderly (ASKmeGOC): protocol for a randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Goals of care discussions (GOCDs) occur between patients, substitute decision-makers, and healthcare providers to ensure shared decision-making about treatment preferences that are aligned with both patients' values and goals and healthcare provider recommendations. GOCDs promote patient autonomy and high-value care. This study will examine the real-world effectiveness of using a novel ASKmeGOC e-tool (ASKmeGOC e-tool) to support nursing-facilitated GOCDs with patients and their substitute decision-makers. We hypothesize that these GOCDs will be acceptable to patients and attending physicians, promote more informed decisions about life-sustaining treatment preferences, and result in fewer undocumented CODE STATUS preferences that will result in both reduced rates of intensive care utilization and direct patient hospitalization costs compared to usual GOCDs conducted by physicians. METHODS: The ASKmeGOC trial is designed as a prospective, single-center, stratified, parallel group, allocation concealed, assessor masked, randomized, pragmatic, mixed-method, and comparative effectiveness trial. This study will enrol all consecutive hospitalized patients ≥ 80 years old. Patients will be randomized 1:1 to either the ASKmeGOC intervention or usual GOCD control group. The primary outcomes include total days of intensive care, ventilator and dialysis utilization during both the index hospitalization, and at 12 months post-admission. The hypothesized sample size is 950 participants per group to demonstrate a reduction in utilization of ≥ 20%. Count regression models will be used to analyze the primary outcomes using an intention-to-treat approach. DISCUSSION: Ensuring patient autonomy in medical decision-making is an essential human right, and GOCDs are critical to ensure patient-centered medical care. This study will evaluate the real-world effectiveness of a novel tool to facilitate GOCDs by non-physician healthcare providers. If shown to be effective, the plan is to spread and scale the tool to other acute care hospitals, primary care practices, and assisted living facilities to evaluate the implementation effectiveness of its use in different healthcare environments by different healthcare providers with the goal of ensuring equitable access to standardized GOCDs to all eligible patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT06002113. Registered on July 27, 2023.
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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 | 0.062 | 0.066 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.077 | 0.014 |
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".