Republished: Which questions of two commonly used multidimensional palliative care patient reported outcome measures are most useful? Results from the European and African PRISMA survey
Bibliographic record
Abstract
AIM: To evaluate the views of clinicians and researchers on their use of outcome measures and which questions are most important in palliative and end-of-life care. METHODS: Online survey of professionals working in clinical care, clinical audit and research in palliative care across Europe and Africa identified through national and international associations and databases. Questions focused on measures used, reasons and which questions were important in two commonly used multidimensional measures, the Palliative care Outcome Scale (POS) and the Support Team Assessment Schedule (STAS). RESULTS: The overall completion rate was 59% (392/663). Three outcome measures were commonly used by over one in four respondents for clinical practice and over one in 10 for research: the Karnofsky Performance Scale (KPS), followed by the Edmonton Symptom Assessment Scale (ESAS) and the POS. Measures were used twice as often in clinical practice as in research. The main uses were similar: assessing patients' symptoms/needs (88% and 85% of POS and STAS users, respectively), monitoring changes (62%, 58%), evaluating care (61%, 48%) and assessing family needs (59%, 60%). Respondents rated the most important questions as pain, symptoms, emotional and family aspects. There were no differences in the choice of the most important questions between doctors and nurses or between researchers and clinicians. CONCLUSIONS: In palliative care, outcome measures often used in clinical practice are also often used in research. Questions relating to pain, symptoms, emotional needs and family concerns are consistently considered the most useful and important in palliative patient reported outcome measures (PROMs).
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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 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".