Variation in suspected cancer referral pathways in primary care: comparative analysis across the International Benchmarking Cancer Partnership
Bibliographic record
Abstract
BACKGROUND: International variations in cancer outcomes persist and may be influenced by differences in the accessibility and organisation of cancer patient pathways. More evidence is needed to understand to what extent variations in the structure of primary care referral pathways for cancer investigation contribute to differences in the timeliness of diagnoses and cancer outcomes in different countries. AIM: To explore the variation in primary care referral pathways for the management of suspected cancer across different countries. DESIGN AND SETTING: Descriptive comparative analysis using mixed methods across the International Cancer Benchmarking Partnership (ICBP) countries. METHOD: Schematics of primary care referral pathways were developed across 10 ICBP jurisdictions. The schematics were initially developed using the Aarhus statement (a resource providing greater insight and precision into early cancer diagnosis research) and were further supplemented with expert insights through consulting leading experts in primary care and cancer, existing ICBP data, a focused review of existing evidence on the management of suspected cancer, published primary care cancer guidelines, and evaluations of referral tools and initiatives in primary care. RESULTS: Referral pathway schematics for 10 ICBP jurisdictions were presented alongside a descriptive comparison of the organisation of primary care management of suspected cancer. Several key areas of variation across countries were identified: inflexibility of referral pathways, lack of a managed route for non-specific symptoms, primary care practitioner decision-making autonomy, direct access to investigations, and use of emergency routes. CONCLUSION: Analysing the differences in referral processes can prompt further research to better understand the impact of variation on the timeliness of diagnoses and cancer outcomes. Studying these schematics in local contexts may help to identify opportunities to improve care and facilitate discussions on what may constitute best referral practice.
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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 | 0.046 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".