Models of Follow-Up Care and Secondary Prevention Measures for Survivors of Colorectal Cancer: Evidence-Based Guidelines and Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To provide recommendations for preferred models of follow-up care for stage I-IV colorectal (CRC) cancer survivors in Ontario; to identify signs and symptoms of potential recurrence and when to investigate; and to evaluate patient information and support needs during the post-treatment survivorship period. METHODS: Consistent with the Program in Evidence-Based Medicine's standardized approach, MEDLINE, EMBASE, PubMed, Cochrane Library, and PROSPERO databases were systematically searched. The authors drafted recommendations and revised them based on the comments from internal and external reviewers. RESULTS: Four guidelines, three systematic reviews, three randomized controlled trials, and three cohort studies provided evidence to develop recommendations. CONCLUSIONS: Colorectal cancer follow-up care is complex and requires multidisciplinary, coordinated care delivered by the cancer specialist, primary care provider, and allied health professionals. While there is limited evidence to support a shared care model for follow-up, this approach is deemed to be best suited to meet patient needs; however, the roles and responsibilities of care providers need to be clearly defined, and patients need to know when and how to contact them. Although there is insufficient evidence to recommend any individual or combination of signs or symptoms as strong predictor(s) of recurrence, patients should be educated about these and know which care provider to contact if they develop any new or concerning symptoms. Psychosocial support and empathetic, effective, and coordinated communication are most valued by patients for their post-treatment follow-up care. Continuing professional education should emphasize the importance of communication skills and coordination of communication between the patient, family, and healthcare providers.
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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 | 0.026 | 0.099 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| 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".