Bibliographic record
Abstract
Abstract How prognosis facilitates cancer control in unbiased (entire) cancer populations is the subject of this chapter. Prognosis applies to the period posttreatment and relates to the entire period postdiagnosis. Changes in population‐level cancer incidence are therefore used to measure the impact of prevention strategies. As implied in the definition, “those who develop cancer” are the group to whom improvements in the probability of cure, survival, and quality of life are targeted. These outcomes are the key ones in this study of the determination and impact of prognostic factors. Prevention through screening (early detection) and effective treatment are key interventions. The concept of prevention is best defined in the context of levels, traditionally called primary, secondary, and tertiary prevention. In the cancer setting, it is more aptly defined thus: secondary prevention as early detection and treatment, and tertiary prevention as targeting prolonged survival and increased quality of life. Secondary and tertiary prevention are aimed at cancer patient populations. Cancer registries and tumor banks are increasingly aware of their role in understanding cancer control factors that describe the care received and access to care in a population and these can be more relevant. Prevention spans the disease trajectory and linking cancer control to prevention reminds us that cancer control is a public health activity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".