Referral challenges for early-onset colorectal cancer: a qualitative study in UK primary care
Bibliographic record
Abstract
BACKGROUND: The incidence of early-onset colorectal cancer (EOCRC) in adults aged <50 years has increased in several Western nations. National surveys have highlighted significant barriers to accessing timely care for patients with EOCRC, which may be contributing to a late stage of presentation in this population group. AIM: To explore awareness of the increasing incidence of EOCRC, and to understand the potential barriers or facilitators faced by GPs when referring younger adults to secondary care with features indicative of EOCRC. DESIGN & SETTING: Qualitative methodology, via virtual semi-structured interviews with 17 GPs in Northern Ireland. METHOD: Reflective thematic analysis was conducted with reference to Braun and Clarke's framework. RESULTS: Three main themes were identified among participating GPs: awareness, diagnostic, and referral challenges. Awareness challenges focused on perceptions of EOCRC being solely associated with hereditary cancer syndromes, and colorectal cancer being a condition of older adults. Key diagnostic challenges centred around the commonality of lower gastrointestinal complaints and overlap in EOCRC symptoms with benign conditions. Restrictions in age-based referral guidance and a GP 'guilt complex' surrounding over-referral to secondary care summarised the referral challenges. Young females were perceived as being particularly disadvantaged with regard to delays in diagnosis. CONCLUSION: This novel research outlines potential reasons for the diagnostic delays seen in patients with EOCRC from a GP perspective, and highlights many of the complicating factors that contribute to the diagnostic process.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".