Cancer control-planning and monitoring population-based systems
Bibliographic record
Abstract
Cancer is a growing global health issue, and many countries are ill-prepared to deal with their current cancer burden let alone the increased burden looming on the horizon. Growing and aging populations are projected to result in dramatic increases in cancer cases and cancer deaths particularly in low- and middle-income countries. It is imperative that planning begin now to deal not only with those cancers already occurring but also with the larger numbers expected in the future. Unfortunately, such planning is hampered, because the magnitude of the burden of cancer in many countries is poorly understood owing to lack of surveillance and monitoring systems for cancer risk factors and for the documentation of cancer incidence, survival and mortality. Moreover, the human resources needed to fight cancer effectively are often limited or lacking. Cancer diagnosis and cancer care services are also inadequate in low- and middle-income countries. Late-stage presentation of cancers is very common in these settings resulting in less potential for cure and more need for symptom management. Palliative care services are grossly inadequate in low- and middle-income countries, and many cancer patients die unnecessarily painful deaths. Many of the challenges faced by low- and middle-income countries have been at least partially addressed by higher income countries. Experiences from around the world are reviewed to highlight the issues and showcase some possible solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".