The Search for a Functional Outcome Measure for Physical Therapy in Specialist Palliative Care: An Ongoing Journey
Bibliographic record
Abstract
A perspective article on the field testing of outcome measures and functional assessment tools by physical therapists working in specialist palliative care. Palliative care physical therapy is an evolving field, and there is a pressing need to evaluate interventions. The authors are members of a specialist palliative care physical therapy team in Ireland who evaluated their service by conducting several research and quality improvement activities. This involved trialing the use of a number of outcome measures, including functional, global, patient-specific, and quality-of-life scales. The following tools were piloted: the Edmonton Functional Assessment Tool, Second Version; an adjusted version of the Functional Independence Measure, Timed Up and Go test; Five Times Sit to Stand test; a self-devised Mobility Measure; distress thermometer; Patient-Specific Functional Scale; and EORTC-QLC C30. This article outlines the journey toward finding the most clinically useful outcome measure to use with palliative care patients. All the tools that were trialed had disadvantages, and many were not suitable for use on sizeable cohorts in our palliative care population. The team concluded that a functional outcome measure was the measure most suitable for measuring the effect of physical therapy interventions and that there was a need to devise a new functional measure specifically for palliative care. The next stage is to use pragmatic clinician input in devising a clinically useful tool in collaboration with research-based content experts to ensure acceptable psychometric properties. It is hoped that this approach will advance the evidence for physical therapy in specialist and general palliative care.
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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 | 0.169 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".