Single- versus multiple-item assessment of quality of life in patients with advanced cancer
Bibliographic record
Abstract
e20528 Background: The main objective of palliative cancer care is to improve quality of life (QOL). As multiple dimensions impact on the construct of QOL, multi-dimensional instruments are usually used in its measurement. These are time consuming and burdensome for repeated use. Recent authors have suggested that brief single-item global assessments can provide a reliable measure of QOL. We assessed the performance of the Edmonton Symptom Assessment System ‘feeling of well-being’ item (ESAS WB) using the Functional Assessment of Cancer Therapy - General (FACT-G) instrument as a gold standard. Methods: After obtaining IRB approval, we reviewed the data from 213 advanced cancer patients who had participated in six studies from March 2006 to June 2008 and determined the level of association between baseline ESAS WB and FACT-G total score and subscale domains (Physical (Pwb), Social/Family (Swb), Emotional (Ewb), and Functional (Fwb) Well-Being) and also the 9 ESAS symptom intensity scores using Spearman correlation coefficients. We also calculated the change between the baseline (T1) and second (T2) observations of ESAS WB and of FACT-G total score and determined their level of association using a Pearson correlation coefficient. In addition, we predicted the change in FACT-G as predicted by the change in ESAS WB score using regression analysis. Results: Mean age was 60 (SD 12) years and 48% were female. At T1, the Spearman correlation coefficient of ESAS WB and FACT-G was -0.48 (p<0.0001). Spearman correlation coefficients for ESAS WB and FACT-G subscale domains and ESAS symptom intensity scores were also highly significant (p<0.0001) for all physical and emotional symptoms (other than p=0.003 for nausea) except for FACT Swb (p=0.08). The Pearson correlation coefficient for difference between T1 and T2 in ESAS WB and FACT-G for 146 patients was -0.36 (p<0.0001). The regression analysis was highly significant (p<0.0001). The change in ESAS WB corresponding to FACT-G published minimally important difference (MID) was -0.24 for 3, -1.55 for 5, and -2.87 for 7, respectively. Conclusions: ESAS WB is a practical instrument for clinical use and best reflects the Pwb, Ewb and Fwb domains of FACT-G as compared to Swb. No significant financial relationships to disclose.
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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 | 0.007 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".