Exploring Patients’ Perspectives on Late Complications after Colorectal and Anal Cancer Treatment: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Patients often experience late complications following treatment for colorectal and anal cancer. Although several measurement tools exist to classify the severity of these symptoms, little is known about how patients personally experience and adapt to these complications. This study aimed to investigate patients' experiences and coping strategies in relation to these symptoms. METHODS: We conducted an explorative qualitative interview study to gather data. RESULTS: Our findings revealed two main categories: How patients react after treatment for colorectal and anal cancer, and Experienced symptoms. Additionally, we identified four sub-categories: the period after discharge, coping strategies, stool symptoms, and other symptoms. Patients commonly feel abandoned once their surgical and oncological treatments are completed. It is typical for patients to turn to the internet for guidance on managing late complications, despite being aware that evidence-based options are limited. Stool-related issues significantly impact patients' personal and professional lives, requiring constant preparedness for accidents, the use of diapers, and the need for extra clothing at all times. Furthermore, patients experience additional troublesome symptoms such as urinary incontinence, fatigue, pain, and sexual dysfunction, which further affect their daily lives. CONCLUSIONS: Patients experience multiple problems after colorectal cancer surgery, and this warrants more focused attention.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".