Bibliographic record
Abstract
PURPOSE OF REVIEW: The transition from primary cancer treatment to posttreatment follow-up care is seen as critical to the long-term health of survivors. However, relatively little attention has been paid to understanding this pivotal period. This review will offer a brief outline of the significant work surrounding this pivotal time published in the past year. RECENT FINDINGS: The growing number of cancer survivors has stimulated an emphasis on finding new models of care, whereby responsibility for survivorship follow-up is transitioned to primary care providers. A variety of models and tools have emerged for follow-up care. Survivorship care plans are heralded as a key component of survivorship care and a vehicle for supporting transition. Uptake of survivorship care plans and implementation of evidence-based models of survivorship care has been slow, hindered by a range of barriers. SUMMARY: Evaluation is needed regarding survivorship models in terms of feasibility, survivor friendliness, cost effectiveness, and achievement of sustainable outcomes. How, and when, to introduce plans for transition to the patient and determine transition readiness are important considerations but need to be informed by evidence. Additional study is needed to identify best practice for the introduction and application of survivorship care plans.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".