Palliative Care Outcome Scale Assessment for Cancer Patients Eligible for Palliative Care: Perspectives on the Relationship between Patient-Reported Outcome and Objective Assessments
Bibliographic record
Abstract
(1) Background: The importance of patient-reported outcome (PRO), i.e., prioritizing patient voice, has increased in cancer treatment, as well as palliative and supportive settings. The Integrated Palliative Care Outcome Scale (IPOS), a hybrid evaluation consisting of “patient evaluation” (PRO) and “peer evaluation” by medical professionals, was developed as a successor version of the Support Team Assessment Schedule (STAS) in 2013 and has been utilized worldwide. The Japanese version of the IPOS (IPOS-J) was developed and released in 2019. The purpose of this study was to explore the applicability of the IPOS-J to clinical practice in the future. (2) Methods: We conducted the following two studies with terminally ill cancer patients: (i) Can an evaluation with the IPOS-J performed by medical professionals (peer evaluation) replace the STAS-J evaluation? (ii) Can the quality of palliative care improve by combining the IPOS-J patient evaluation with the peer evaluation? (3) Results: The overall intervention rate and urgent intervention rate for the STAS-J and IPOS-J was 34.4 vs. 34.1% (p = 0.91) and 10.4 vs. 9.9% (p = 0.78), respectively. The patients selected “intervention required” but the medical professionals selected “no intervention required” in 47 cases. The medical team performed appropriate intervention after re-assessment. As a result, more than 70% of the patients were “intervention-free” after 1 week of intervention. (4) Conclusions: The IPOS-J peer evaluation was as useful as the STAS-J evaluation. A hybrid type of evaluation, combining patient evaluation (PRO) and peer evaluation, may help us to understand patient needs and improve the quality of 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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".