Evaluating Survivorship Care Plans: Results of a Randomized, Clinical Trial of Patients With Breast Cancer
Bibliographic record
Abstract
PURPOSE: An Institute of Medicine report recommends that patients with cancer receive a survivorship care plan (SCP). The trial objective was to determine if an SCP for breast cancer survivors improves patient-reported outcomes. PATIENTS AND METHODS: Women with early-stage breast cancer who completed primary treatment at least 3 months previously were eligible. Consenting patients were allocated within two strata: less than 24 months and ≥ 24 months since diagnosis. All patients were transferred to their own primary care physician (PCP) for follow-up. In addition to a discharge visit, the intervention group received an SCP, which was reviewed during a 30-minute educational session with a nurse, and their PCP received the SCP and guideline on follow-up. The primary outcome was cancer-related distress at 12 months, assessed by the Impact of Event Scale (IES). Secondary outcomes included quality of life, patient satisfaction, continuity/coordination of care, and health service measures. RESULTS: Overall, 408 survivors were enrolled through nine tertiary cancer centers. There were no differences between groups on cancer-related distress or on any of the patient-reported secondary outcomes, and there were no differences when the two strata were analyzed separately. More patients in the intervention than control group correctly identify their PCP as primarily responsible for follow-up (98.7% v 89.1%; difference, 9.6%; 95% CI, 3.9 to 15.9; P = .005). CONCLUSION: The results do not support the hypothesis that SCPs are beneficial for improving patient-reported outcomes. Transferring follow-up to PCPs is considered an important strategy to meet the demand for scarce oncology resources. SCPs were no better than a standard discharge visit with the oncologist to facilitate transfer.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".