Future of population‐based cancer registries: A global perspective—A survey of population‐based cancer registries
Bibliographic record
Abstract
Population-Based Cancer Registries (PBCRs) play a fundamental role in cancer control. They collect data to compile information on the occurrence, extent, and outcome of cancer in geographically defined populations. Whilst the basic reporting on cancer incidence and survival by cancer type is extremely useful, other prognostic factors including stage, biomarkers, comorbidities, treatment, and socio-economic parameters are crucial to evaluate, understand, and ultimately reduce the variation of outcomes observed in different populations. To explore current data collection practices of the PBCRs worldwide and comprehend their challenges and future directions, we conducted a web-based survey in collaboration with the Global Initiative for Cancer Registry Development (GICR) led by the International Agency for Research on Cancer (IARC), the International Association of Cancer Registries (IACR), and the North American Association of Central Cancer Registries (NAACCR). Of the 268 invited PBCRs, 141 PBCRs responded to our survey. Although almost all PBCRs reported collecting the basic variables for each cancer (incidence date, basis of diagnosis, topography, morphology and tumour behaviour), fewer collect date of death, stage, treatment, biomarkers, and socio-economic parameters, this issue being more pronounced in LMIC. Most PBCRs confirmed reporting on cancer incidence, but only 60% publish survival results. Other outcomes such as recurrence and patient-reported outcome measures were rarely available. There is a clear need for development and sustained support to maximize the PBCRs' ability to collect data and/or expand coverage area, ideally through significant investment in legislation, financial, human, and technological resources to secure and optimize their potential in cancer control.
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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 | 0.105 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".