Agreement between questionnaires and registry data on routes to diagnosis and milestone dates of the cancer diagnostic pathway
Bibliographic record
Abstract
BACKGROUND: The routes to diagnosis and the time intervals along the diagnostic pathway affect cancer outcomes. Some data on routes to diagnosis and milestone dates can be extracted from registries or databases. When this data is incomplete, inaccurate or non-existing, other data sources are needed. This study investigates the agreement between multiple data sources on routes to diagnosis and milestone dates of cancer pathway. METHODS: Information on routes to diagnosis and milestone dates were compared across four data sources (cancer patients, general practitioners, cancer specialists and registries) for breast, colorectal, lung and ovarian cancers across the UK, Scandinavia, Canada and Australia. Agreement on routes to diagnosis and milestone dates was assessed by Kappa and AC1 coefficients and Lin's concordance correlation coefficient (CCC). RESULTS: 4502 patients were included in the analysis of routes to diagnosis. The agreement was almost perfect (kappa = 0.15-0.88, AC1 = 0.86-0.91) for breast cancer, substantial to almost perfect (kappa = 0.07-0.86, AC1 = 0.74-0.93) for colorectal and ovarian cancers, and substantial (kappa = 0.09-0.11, AC1 = 0.65-0.74) for lung cancer. 2287 patients were included in the analysis of milestone dates. The agreement was adequate for all cancer types (CCC = 0.88-0.99); highest agreement was seen for date of diagnosis (CCC = 0.94-0.99). CONCLUSION: We found a reasonable agreement between patient/physician questionnaires and registry data for routes to diagnosis and milestone dates. The agreement on routes to diagnosis was generally higher for breast cancer than for colorectal, ovarian and lung cancers. Lower agreement was seen on date of first presentation to primary care and date of treatment initiation compared to date of diagnosis.
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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 | 0.053 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".