Implementing a mHealth intervention to increase colorectal cancer screening among high-risk cancer survivors treated with radiotherapy in the Childhood Cancer Survivor Study (CCSS)
Bibliographic record
Abstract
BACKGROUND: Cancer survivors treated with any dose of radiation to the abdomen, pelvis, spine, or total body irradiation (TBI) are at increased risk for developing colorectal cancer (CRC) compared to the general population. Since earlier detection of CRC is strongly associated with improved survival, the Children's Oncology Group (COG) Long-Term Follow-Up Guidelines recommend that these high-risk cancer survivors begin CRC screening via a colonoscopy or a multitarget stool DNA test at the age of 30 years or 5 years following the radiation treatment (whichever occurs last). However, only 37% (95% CI 34.1-39.9%) of high-risk survivors adhere to CRC surveillance. The Activating cancer Survivors and their Primary care providers (PCP) to Increase colorectal cancer Screening (ASPIRES) study is designed to assess the efficacy of an intervention to increase the rate of CRC screening among high-risk cancer survivors through interactive, educational text-messages and resources provided to participants, and CRC screening resources provided to their PCPs. METHODS: ASPIRES is a three-arm, hybrid type II effectiveness and implementation study designed to simultaneously evaluate the efficacy of an intervention and assess the implementation process among participants in the Childhood Cancer Survivor Study (CCSS), a North American longitudinal cohort of childhood cancer survivors. The Control (C) arm participants receive electronic resources, participants in Treatment arm 1 receive electronic resources as well as interactive text messages, and participants in Treatment arm 2 receive electronic educational resources, interactive text messages, and their PCP's receive faxed materials. We describe our plan to collect quantitative (questionnaires, medical records, study logs, CCSS data) and qualitative (semi-structured interviews) intervention outcome data as well as quantitative (questionnaires) and qualitative (interviews) data on the implementation process. DISCUSSION: There is a critical need to increase the rate of CRC screening among high-risk cancer survivors. This hybrid effectiveness-implementation study will evaluate the effectiveness and implementation of an mHealth intervention consisting of interactive text-messages, electronic tools, and primary care provider resources. Findings from this research will advance CRC prevention efforts by enhancing understanding of the effectiveness of an mHealth intervention and highlighting factors that determine the successful implementation of this intervention within the high-risk cancer survivor population. TRIAL REGISTRATION: This protocol was registered at clinicaltrials.gov (identifier NCT05084833 ) on October 20, 2021.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".