Preliminary Results of the Generation of a Shortened Quality-of-Life Assessment for Patients with Advanced Cancer: The FACIT-Pal-14
Bibliographic record
Abstract
OBJECTIVE: Shortened quality-of-life (QOL) tools are advantageous in palliative care patients. Development of such tools begins with the identification of issues relevant to a population. The purpose of this study was to identify the most important items of the Functional Assessment of Chronic Illness Therapy-Palliative Care (FACIT-Pal) to create an abbreviated questionnaire for future palliative care trials. METHODS: A convenience sample of patients and health care professionals (HCPs) assessed the relevance of each item of the FACIT-Pal and whether they would include the item in a final questionnaire. Patients and HCPs identified their top 10 most important issues and were asked whether items were inappropriate, upsetting, or irrelevant; a shortened questionnaire was generated from this input. RESULTS: Sixty patients and 56 HCPs participated. The median score in the Karnofsky Performance Scale (KPS) of patients was 70, and the majority of HCPs were radiation oncologists. The 46-item questionnaire was shortened to 14 questions, retaining several items from the Functional Assessment of Cancer Therapy-General (FACT-G) as well as issues pertaining specifically to palliative care patients. Items within the emotional, physical, and functional well-being subscales were retained along with those for various symptoms including constipation, nausea, dyspnea, and sleep. No new content beyond what is covered by the FACIT-Pal was identified consistently by either HCPs or patients. Similarly, no item was consistently rated as being inappropriate, upsetting, or irrelevant in the 14-item questionnaire. CONCLUSION: The FACIT-Pal-14, a shortened 14-item questionnaire has been generated for the palliative care population. Future studies should complete psychometric validation of this instrument for the assessment of QOL in palliative care patients.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".