Shared experiences of diagnosis and treatment of young-onset colorectal cancer: a patient-oriented qualitative study
Bibliographic record
Abstract
Abstract Objective: The aim of this study was to gain a better understanding of the lived experiences of young-onset colorectal cancer (yCRC) from the perspective of patients and/or caregivers. Methods: We conducted a qualitative study, in collaboration with COLONTOWN®, an online colorectal cancer community. Individuals who have been diagnosed with yCRC, that is below the age of 50 years, or care for an individual with yCRC were invited to complete an online survey primarily comprising of an open-ended question asking participants to share their yCRC experiences in a text box, similar with how they may post on a social media platform. We applied an inductive, qualitative approach to identify themes arising from participants’ experiences. Results: From May to June 2019, we gathered experiences from 109 patients with yCRC and 11 caregivers. The majority of patients with yCRC were female (86, 71.7%) and diagnosed between the ages of 30 and 39 (49, 40.9%) and 40 and 49 years (61, 50.8%). We identified 8 themes: symptoms experienced; being misdiagnosed; advocating for oneself; appreciation of the healthcare team; frustration with the healthcare team and healthcare system; lasting effects of yCRC and its treatment; connecting with others; and reflections on experiences with yCRC. Conclusions: Our study highlights challenges experienced by yCRC patients across diagnosis, during treatment, and after treatment, notably misdiagnosis and need for access to information and support. Our study raises awareness of yCRC and experiences of individuals impacted by this disease.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".