Checklists for Complications During Systemic Cancer Treatment Shared by Patients, Friends, and Health Care Professionals: Prospective Interventional Cohort Study
Bibliographic record
Abstract
BACKGROUND: Advances in cancer management have been associated with an increased incidence of emergency presentations with disease- or treatment-related complications. OBJECTIVE: This study aimed to measure the ability of patients and members of their social network to complete checklists for complications of systemic treatment for cancer and examine the impact on patient-centered and health-economic outcomes. METHODS: A prospective interventional cohort study was performed to assess the impact of a smartphone app used by patients undergoing systemic cancer therapy and members of their network to monitor for common complications. The app was used by patients, a nominated "safety buddy," and acute oncology services. The control group was made up of patients from the same institution. Measures were based on process (completion of checklists over 60 days), patient experience outcomes (Hospital Anxiety and Depression Scale and the General version of the Functional Assessment of Cancer Therapy at baseline, 1 month, and 2 months) and health-economic outcomes (usage of appointments in primary care and elective and unscheduled hospital admissions). RESULTS: At the conclusion of the study, 50 patients had completed 2882 checklists, and their 50 "safety buddies" had completed 318 checklists. Near daily usage was maintained over the 60-day study period. When compared to a cohort of 50 patients with matching disease profiles from the same institution, patients in the intervention group had comparable changes in Hospital Anxiety and Depression Scale and General version of the Functional Assessment of Cancer Therapy. Patients in the Intervention Group required a third (32 vs 97 nights) of the hospital days with overnight stay compared to patients in the Control Group, though the difference was not significant. The question, "I feel safer with the checklist," received a mean score of 4.27 (SD 0.87) on a Likert scale (1-5) for patients and 4.55 (SD 0.65) for family and friends. CONCLUSIONS: Patients undergoing treatment for cancer and their close contacts can complete checklists for common complications of systemic treatments and take an active role in systems supporting their own safety. A larger sample size will be needed to assess the impact on clinical outcomes and health economics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".