After the Treatment Phase of Colorectal Cancer Care: Survivorship and Follow-Up
Bibliographic record
Abstract
The number of long-term colorectal cancer (CRC) survivors has increased substantially over the past three decades due to both ongoing advances in early detection and improvements in cancer therapies. Adult survivors of CRC experience chronic health conditions due to normal issues associated with aging, which is further compounded by the long-term adverse effects of having had cancer and anti-cancer therapies. In addition, they are at a higher risk for CRC recurrences, new primary cancers, and other co-morbidities. This article will provide an overview of the clinical care of adult survivors of CRC. Epidemiologic data will be presented followed by a discussion of the approach to the care of long-term adult survivors of CRC, including surveillance of recurrences and new primary cancers, interventions to manage both physical and psychological consequences of cancer and its treatments, and strategies to address concerns related to unemployment and disability. Finally, we will explore the challenges of healthcare delivery, especially with respect to the coordination of follow-up between cancer specialists and primary care physicians, so as to ensure that all of the survivor’s health needs are met promptly and appropriately.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".