Frailty in older Turkish cancer patients undergoing post-surgical adjuvant chemotherapy
Bibliographic record
Abstract
BACKGROUND: Understanding the frailty levels of older patients undergoing surgery and chemotherapy will contribute to timely and reliable care practices and improve care outcomes. AIMS: To determine the frailty of cancer patients who received chemotherapy treatment after surgery. METHODS: This descriptive study included 192 Turkish patients aged over 60 years who received chemotherapy after surgery for cancer. Data were collected using a patient survey and the Edmonton Frailty Scale. RESULTS: The average age of the participants was 66.3±5.3 years. Around 40% (40.6%) of the sample were diagnosed with breast cancer. The Edmonton Frailty Scale score of the group was 6.6 (SD±3.7). A quarter of the sample (22.9%) were at risk of frailty. Frailty levels were higher in older individuals with gastrointestinal cancers and other cancer groups compared with patients with breast cancer (p<0.001); patients with additional chronic diseases other than cancer (p=0.004); and in those with a history of falling and hospitalisation in the past year (p<0.001). CONCLUSIONS: Older patients with gastrointestinal cancer, additional chronic disease and a history of falling and hospitalisation within the past year should be evaluated closely for frailty before and during chemotherapy. It is crucial to consider the patient's vulnerability when making care and treatment decisions for older patients with cancer. Understanding the frailty levels of older patients who undergo surgery and receive chemotherapy can help health professionals to decide on timely and reliable care practices and improve care outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".