Validation of quality indicators for end-of-life communication: results of a multicentre survey
Bibliographic record
Abstract
BACKGROUND: The lack of validated quality indicators is a major barrier to improving end-of-life communication and decision-making. We sought to show the feasibility of and provide initial validation for a set of quality indicators related to end-of-life communication and decision-making. METHODS: We administered a questionnaire to patients and their family members in 12 hospitals and asked them about advance care planning and goals-of-care discussions. Responses were used to calculate a quality indicator score. To validate this score, we determined its correlation with the concordance between the patients’ expressed wishes and the medical order for life-sustaining treatments recorded in the hospital chart. We compared the correlation with concordance for the advance care planning component score with that for the goal-of-care discussion scores. RESULTS: We enrolled 297 patients and 209 family members. At all sites, both overall quality indicators and individual domain scores were low and there was wide variability around the point estimates. The highest-ranking institution had an overall quality indicator score (95% confidence interval) of 40% (36%–44%) and the lowest had a score of 18% (11%–25%). There was a strong correlation between the overall quality indicator score and the concordance measure (r = 0.72, p = 0.008); the estimated correlation between the advance care planning score and the concordance measure (r = 0.35) was weaker than that between the goal-of-care discussion scores and the concordance measure (r = 0.53). INTERPRETATION: Quality of end-of-life communication and decision-making appears low overall, with considerable variability across hospitals. The proposed quality indicator measure shows feasibility and partial validity. Study registration: ClinicalTrials.gov, no. NCT01362855
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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 | 0.046 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".