Results of a cluster randomized trial to evaluate a nursing lead supportive care intervention in newly diagnosed breast and colorectal cancer patients.
Bibliographic record
Abstract
6035 Background: Patient transitions during the early phases of cancer care from initial diagnosis through oncology consultation are often poorly coordinated resulting in unmet need, poor continuity, and resultant distress. It has been proposed that better coordination of care during this period would improve the care experience from the patient’s perspective. We designed a randomized trial to test a community based nursing lead coordination of care intervention in newly diagnosed breast and colorectal cancer patients. Methods: Cluster randomized control trial in 193 newly diagnosed breast and colorectal cancer patients enrolled through surgical practices within 7 days of cancer surgery in Toronto, Canada. Surgical practices were randomized between a standardized nursing intervention and a control group involving usual care practices. The intervention consisted of a standardized in person supportive care assessment with ongoing supportive care by telephone or in person that included linkage to community services using protocol specified guidelines according to identified needs. The primary outcomes measured at 8 weeks were validated patient reported outcomes (PROs) of 1) unmet need (SCNS) and 2) continuity of care (CCCQI). Secondary outcomes included 1) quality of life (EORTC QLQ-C30), 2) health resource utilization, and 3) level of uncertainty with care trajectory (MUIS) at 8 weeks. Results: 121 breast and 72 colorectal patients were randomized through 28 surgical practices. The intervention group had a median of 6 nursing contacts over the study period. There were no differences between groups on PROs of unmet need, continuity of care, quality of life, or uncertainty. Health service utilization did not differ between groups. Conclusions: A specialized oncology nursing intervention early in the care trajectory did not result in improved supportive care outcomes for patients. [Table: see text]
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".