Usefulness of Frailty Markers in the Assessment of the Health and Functional Status of Older Cancer Patients Referred for Chemotherapy: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Older cancer patients seen in an oncology clinic seem to be healthier and less disabled than traditional geriatric patients. Choosing the most sensitive tools to assess their health status is a major issue. This cross-sectional study explores the usefulness of frailty markers in detecting vulnerability in older cancer patients. METHODS: The study included cancer patients >or=70 years old referred to an oncology clinic for chemotherapy. Information on comorbidities, disability in instrumental activities of daily living (IADL) and activities of daily living (ADL), and seven frailty markers (nutrition, mobility, strength, energy, physical activity, mood, and cognition) was collected. Patients were classified into four hierarchical groups: 1- No frailty markers, IADL, or ADL disability; 2- Presence of frailty markers without IADL or ADL disability; 3- IADL disability without ADL disability; 4- ADL disability. RESULTS: Among the 50 patients assessed, 6 (12.0%) were classified into Group 1, 21 (42.0%) into Group 2, 15 (30.0%) into Group 3, and 8 (16.0%) into Group 4. In Group 2, 7 patients (33.3 %) had one frailty marker, and 14 (66.7%) had two or more. The most prevalent of the frailty markers were nutrition, mobility, and physical activity. CONCLUSION: The assessment of seven frailty markers allowed the detection of potential vulnerability among 42% of older cancer patients that would not have been detected through an assessment of IADL and ADL disability alone. A longitudinal study is needed to determine whether the use of frailty markers can better characterize the older cancer population and predict adverse outcomes due to cancer treatment.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".