BABEL (Better tArgeting, Better outcomes for frail ELderly patients) advance care planning: a comprehensive approach to advance care planning in nursing homes: a cluster randomised trial
Bibliographic record
Abstract
BACKGROUND: Nursing home (NH) residents should have the opportunity to consider, discuss and document their healthcare wishes. However, such advance care planning (ACP) is frequently suboptimal. OBJECTIVE: Assess a comprehensive, person-centred ACP approach. DESIGN: Unblinded, cluster randomised trial. SETTING: Fourteen control and 15 intervention NHs in three Canadian provinces, 2018-2020. SUBJECTS: 713 residents (442 control, 271 intervention) aged ≥65 years, with elevated mortality risk. METHODS: The intervention was a structured, $\sim$60-min discussion between a resident, substitute decision-maker (SDM) and nursing home staff to: (i) confirm SDMs' identities and role; (ii) prepare SDMs for medical emergencies; (iii) explain residents' clinical condition and prognosis; (iv) ascertain residents' preferred philosophy to guide decision-making and (v) identify residents' preferred options for specific medical emergencies. Control NHs continued their usual ACP processes. Co-primary outcomes were: (a) comprehensiveness of advance care planning, assessed using the Audit of Advance Care Planning, and (b) Comfort Assessment in Dying. Ten secondary outcomes were assessed. P-values were adjusted for all 12 outcomes using the false discovery rate method. RESULTS: The intervention resulted in 5.21-fold higher odds of respondents rating ACP comprehensiveness as being better (95% confidence interval [CI] 3.53, 7.61). Comfort in dying did not differ (difference = -0.61; 95% CI -2.2, 1.0). Among the secondary outcomes, antimicrobial use was significantly lower in intervention homes (rate ratio = 0.79, 95% CI 0.66, 0.94). CONCLUSIONS: Superior comprehensiveness of the BABEL approach to ACP underscores the importance of allowing adequate time to address all important aspects of ACP and may reduce unwanted interventions towards the end of life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".