The Selection and Use of Outcome Measures in Palliative and End-of-Life Care Research: The MORECare International Consensus Workshop
Bibliographic record
Abstract
CONTEXT: A major barrier to widening and sustaining palliative care service provision is the requirement for better selection and use of outcome measures. Service commissioning is increasingly based on patient, carer, and service outcomes as opposed to service activity. OBJECTIVES: To generate recommendations and consensus for research in palliative and end-of-life care on the properties of the best outcome measures, enhancing the validity of proxy-reported data and optimal data collection time points. METHODS: An international expert "workshop" was convened and an online consensus survey was undertaken using the MORECare Transparent Expert Consultation to generate recommendations and level of agreement. We focused on three areas: 1) measurement properties, 2) use of proxies, and 3) measurement timing. Data analysis comprised descriptive analysis of aggregate scores and collation of narrative comments. RESULTS: There were 31 workshop attendees; 29 recommendations were included in the online survey, completed by 28 experts. The top three recommendations by area were the following: 1) the properties of the best outcome measures are responsive to change over time and capture clinically important data, 2) to enhance the validity of proxy data requires clear and specific guidelines to aid lay individuals' and/or professionals' completion of proxy measures, and 3) data collection time points need clear identification to establish a baseline. CONCLUSION: Outcome measurement in palliative and end-of-life care requires the use of psychometrically robust measures that are clinically responsive, with defined data collection time points to establish a baseline and clear administration guidelines to complete proxy measures. To further the field requires clinical imperatives to more closely inform recommendations on outcome measurement.
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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 | 0.683 | 0.505 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.022 | 0.026 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".