Text Messaging in Cancer-Supportive Care: A Systematic Review
Bibliographic record
Abstract
The global cancer disease burden is substantial, resulting in increased economic and clinical strain on our healthcare systems. A proposed solution is text-based communication, which can be used for cancer-supportive care. We conducted a systematic review to synthesize and describe the use of text-based communications for cancer-supportive care. Our population of interest included adult patients with cancer. A total of 18 studies were included in the review: 9 RCTs and 9 non-randomized interventional/observational studies. Patients were largely satisfied with text-based communication during their cancer care. Compared to controls, results for other outcomes including symptoms and quality of life were largely mixed; however, no harms were observed. Furthermore, positive outcomes were seen for specific interventions, such as text message medication reminders. These findings should be considered with caution due to the considerable heterogeneity observed between studies regarding their design and reported outcomes and the high risk of bias associated with 6/18 studies. Overall, this review suggests that text-based communication may be a complementary tool for cancer-supportive care; however, more research is needed to examine the feasibility of implementation and use.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".