Quality of Life of Colorectal Cancer Patients in Certified Centers versus Non-Certified Hospitals
Bibliographic record
Abstract
BACKGROUND: Since 2006, in Germany colorectal cancer patients can be treated in certified colorectal cancer centers. The aim of this explorative study was to investigate whether there are differences in the quality of life (QoL) of colorectal cancer patients who were treated in certified versus noncertified centers. PATIENTS AND METHODS: A total of 284 colorectal cancer patients participated in the study: 184 patients from certified colorectal cancer centers and 100 patients from noncertified centers. Data on QoL (using the Quality of Life Questionnaire of the European Organization for Research and Treatment of Cancer (EORTC-QLQ C30)), patient satisfaction, mental distress and sociodemographic data were assessed with a questionnaire in a written survey after the hospital stay. The moderating influence of patientrelated characteristics (e.g. age, sex, patient satisfaction, and psychological distress) and cancerrelated factors (Union internationale contre le cancer (UICC) stage) were tested. RESULTS: On a descriptive level, patients from noncertified centers had a higher QoL in 5 subdimensions (higher physical and role functioning and less insomnia, appetite loss and financial difficulties). After adjustment, only 2 differences remained significant: physical functioning (p < 0.01) and role functioning (p = 0.02). CONCLUSION: Structural improvements in the oncological care are not necessarily reflected in a better QoL of the patients treated in certified colorectal cancer centers. The findings are discussed in the context of the applied study design.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".