Urgent suspected cancer referrals from general practice: audit of compliance with guidelines and referral outcomes
Bibliographic record
Abstract
BACKGROUND: Late diagnosis contributes to the UK having poorer cancer survival than many countries in Europe. Cancer referral guidelines help GPs decide which patients to refer urgently for further investigation. AIM: To examine primary care referral patterns, compliance with referral guidance, and eventual outcome for patients. DESIGN AND SETTING: Prospective audit within general practice in Scotland. METHOD: GPs in Scotland reviewed all urgent suspected cancer referrals over a 6-month period. They noted the final diagnosis and assessed whether the referral was in accordance with agreed referral guidelines. RESULTS: A total of 18 775 urgent suspected cancer referrals were analysed from 516 GP practices. The referral rate ranged from 3.7 to 24.0 per 1000 per annum; 30.8% of referrals were for patients aged under 50 years, yet this age group accounts for only 11.1% of all diagnosed cancers; 10.3% of all urgent cancer referrals were for suspected melanoma, despite this cancer accounting for only 4.1% of new cancers. The proportion of patients subsequently diagnosed with cancer was greatest for leukaemia (61.7%), prostate (52.6%), and lung cancer referrals (39.7%), and lowest for melanoma (11.8%), oesophago-gastric (11.2%), brain (10.6%), and laryngeal cancer referrals (7.8%). Compliance with referral guidelines was 90.9%. A large proportion of referrals considered to be outside the guidelines still had a cancer diagnosed (urological 15.9%, lung 8.8%, colorectal 8.4%, and breast 6.4%). CONCLUSION: There is wide variation in GP referral rates for suspected cancer with a greater than expected proportion of referrals for younger people. Many referrals considered to be outside the national guidelines were diagnosed with cancer, suggesting factors other than those in referral guidelines alert GPs to the possibility of cancer.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".