Are Patient-Reported Outcomes of Physical Function a Valid Substitute for Objective Measurements?
Bibliographic record
Abstract
Background: Physical function is important for defining treatment strategies in patients with cancer and can be estimated using patient-reported outcomes (pros). Although pros are subjective, physical activity and fitness can be tested objectively with adequate, but more labour-intensive methods that are rarely used in daily clinical practice. To determine whether pros for physical function (pro-pf) accurately predict physical function, we studied their interrelationships with objective measures of physical activity and fitness in patients with cancer who had completed cancer treatment, including adjuvant or neoadjuvant chemotherapy or autologous stem-cell transplantation. Methods: Baseline data from the react (Resistance and Endurance Exercise After Chemotherapy) and exist (Exercise Intervention After Stem-Cell Transplantation) studies were evaluated. In those studies, the effects of an exercise intervention on physical fitness, fatigue, and health-related quality of life in patients with cancer shortly after completion of chemotherapy or stem-cell transplantation were studied. Interrelationships between pro-pf (physical function subscale of the European Organisation for Research and Treatment of Cancer 30-question core Quality of Life Questionnaire), physical activity (accelerometer), and cardiorespiratory fitness (peak oxygen uptake) were assessed using univariable and multivariable multilevel linear mixed-model analyses. Results: After adjustment for age, sex, and body mass index, the pro-pf was significantly associated with physical activity (β = 1.75; 95% confidence interval: 1.08 to 2.42) and cardiorespiratory fitness (β = 0.10; 95% confidence interval: 0.06 to 0.13). Standardized coefficients were 0.28 and 0.26 respectively, indicating a weak association. Conclusions: The pro-pf is only weakly associated with objective physical activity and fitness evaluation in patients after curative treatment for cancer. The pro-pf cannot, therefore, be used in clinical practice as a substitute for objective measures of physical function.
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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 | 0.052 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".