Bibliographic record
Abstract
Cancer screening interventions offer the potential for both risk and benefit. Research related to screening and cancer in children or adolescents/young adults (AYA) can approach any of several questions. One question to be addressed is whether population-based screening tests can be developed that would reduce incidence or mortality from cancer in children or AYA without causing undue risk to the healthy population and with reasonable cost-effectiveness. This has not yet proven to be possible, and some of the relevant considerations are discussed in this article. The second question concerns the use of screening tests commonly applied to the general population and the special considerations when applied in the context of children/AYA with cancer or of adult survivors. Finally, a third general area of research concerns the inclusion of specialized screening in the follow-up of survivors of cancer in children and AYA to address the potential for recurrences, new primaries, and long-term sequelae of treatment. Although current guidelines for screening in follow-up are derived from a blend of evidence and expert clinical opinion, it is likely that future guidelines will evolve as a result of clinically intense research that takes into consideration the needs of this very unique group.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".