A brief, patient- and proxy-reported outcome measure in advanced illness: Validity, reliability and responsiveness of the Integrated Palliative care Outcome Scale (IPOS)
Bibliographic record
Abstract
Background: Few measures capture the complex symptoms and concerns of those receiving palliative care. Aim: To validate the Integrated Palliative care Outcome Scale, a measure underpinned by extensive psychometric development, by evaluating its validity, reliability and responsiveness to change. Design: Concurrent, cross-cultural validation study of the Integrated Palliative care Outcome Scale – both (1) patient self-report and (2) staff proxy-report versions. We tested construct validity (factor analysis, known-group comparisons, and correlational analysis), reliability (internal consistency, agreement, and test–retest reliability), and responsiveness (through longitudinal evaluation of change). Setting/participants: In all, 376 adults receiving palliative care, and 161 clinicians, from a range of settings in the United Kingdom and Germany Results: We confirm a three-factor structure (Physical Symptoms, Emotional Symptoms and Communication/Practical Issues). Integrated Palliative care Outcome Scale shows strong ability to distinguish between clinically relevant groups; total Integrated Palliative care Outcome Scale and Integrated Palliative care Outcome Scale subscale scores were higher – reflecting more problems – in those patients with ‘unstable’ or ‘deteriorating’ versus ‘stable’ Phase of Illness (F = 15.1, p < 0.001). Good convergent and discriminant validity to hypothesised items and subscales of the Edmonton Symptom Assessment System and Functional Assessment of Cancer Therapy–General is demonstrated. The Integrated Palliative care Outcome Scale shows good internal consistency (α = 0.77) and acceptable to good test–retest reliability (60% of items k w > 0.60). Longitudinal validity in form of responsiveness to change is good. Conclusion: The Integrated Palliative care Outcome Scale is a valid and reliable outcome measure, both in patient self-report and staff proxy-report versions. It can assess and monitor symptoms and concerns in advanced illness, determine the impact of healthcare interventions, and demonstrate quality of care. This represents a major step forward internationally for palliative care 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".