Bibliographic record
Abstract
Like in every other aspect of society, digital transformation is impacting cancer control.It is becoming increasingly evident that traditional approaches to data collection and curation will not accommodate the scale of observation and insight extraction needed to unravel the complexity of cancer; hence, data and digitally enabled innovation affect every chapter in this book in one way or another.This section specifically seeks to highlight the demand for the data and technologies to meet the challenges of cancer.Brierley, O'Sullivan and Gospodarowicz highlight the current and growing demand for data in cancer control as we more fully realise the complexity of cancer.While a great deal has been invested in understanding the scale of the global cancer burden, the data available today lacks depth and coverage both for personalised cancer care and for the design of cancer services at large.The important challenges of data privacy, data quality and the need for harmonisation are highlighted while also illuminating the opportunities for coordination and maturity in our thinking on the value of data for society more broadly.Chung provides a detailed perspective on the need and opportunity to mature our approach to data management as a necessary step towards realising the full potential of digitalisation to impact cancer control.Central to this argument is the need for organisations to establish a culture and capacity that nurture data literacy and data stewardship.Such approaches should not be limited to single organisations, but rather work to expand the paradigm and bridge across the multiple scales of health systems -from institutions to health systems and to countriesworking together to address the global challenge of cancer control.Jaffray and Berlin demonstrate the radical pace of change that digitalisation is bringing to cancer care today.Advances in digital connectivity, data capture and computational horsepower are making virtual care and personalised care that accommodate image-and genomics-based assessment and treatment a reality.Artificial intelligence (AI) technologies are also transforming diagnostics and working to increase the efficiency of care and access to expertise in a world of expanding data.This dependence on digital capabilities heightens the impact of cyberattacks and cancer services are among those most severely impacted when cybercriminals are successful.Beyond the care for the individual, digitalisation also brings the potential to combine these datasets with the computational capacity to both design the cancer services of tomorrow and motivate rational investment.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.083 | 0.008 |
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".