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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".