Diagnostic routes and time intervals for ovarian cancer in nine international jurisdictions; findings from the International Cancer Benchmarking Partnership (ICBP)
Bibliographic record
Abstract
BACKGROUND: International Cancer Benchmarking Partnership Module 4 reports the first international comparison of ovarian cancer (OC) diagnosis routes and intervals (symptom onset to treatment start), which may inform previously reported variations in survival and stage. METHODS: Data were collated from 1110 newly diagnosed OC patients aged >40 surveyed between 2013 and 2015 across five countries (51-272 per jurisdiction), their primary-care physicians (PCPs) and cancer treatment specialists, supplement by treatment records or clinical databases. Diagnosis routes and time interval differences using quantile regression with reference to Denmark (largest survey response) were calculated. RESULTS: There were no significant jurisdictional differences in the proportion diagnosed with symptoms on the Goff Symptom Index (53%; P = 0.179) or National Institute for Health and Care Excellence NG12 guidelines (62%; P = 0.946). Though the main diagnosis route consistently involved primary-care presentation (63-86%; P = 0.068), onward urgent referral rates varied significantly (29-79%; P < 0.001). In most jurisdictions, diagnostic intervals were generally shorter and other intervals, in particular, treatment longer compared to Denmark. CONCLUSION: This study highlights key intervals in the diagnostic pathway where improvements could be made. It provides the opportunity to consider the systems and approaches across different jurisdictions that might allow for more timely ovarian cancer diagnosis and treatment.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".