Advanced‐stage cancer and time to diagnosis: An International Cancer Benchmarking Partnership (ICBP) cross‐sectional study
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between tumour stage at diagnosis and selected components of primary and secondary care in the diagnostic interval for breast, colorectal, lung and ovarian cancers. METHODS: Observational study based on data from 6,162 newly diagnosed symptomatic cancer patients from Module 4 of the International Cancer Benchmarking Partnership. We analysed the odds of advanced stage of cancer as a flexible function of the length of primary care interval (days from first presentation to referral) and secondary care interval (days from referral to diagnosis), respectively, using logistic regression with restricted cubic splines. RESULTS: The association between time intervals and stage was similar for each type of cancer. A statistically significant U-shaped association was seen between the secondary care interval and the diagnosis of advanced rather than localised cancer, odds decreasing from the first day onwards and increasing around three and a half months. A different pattern was seen for the primary care interval, flat trends for colorectal and lung cancers and a slightly curved association for ovarian cancer, although not statistically significant. CONCLUSION: The results confirm previous findings that some cancers may progress even within the relatively short time frame of regulated diagnostic intervals. The study supports the current emphasis on expediting symptomatic diagnosis 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".