Innovative Use of mHealth and Clinical Technology for Oncology Clinical Trials in Africa
Bibliographic record
Abstract
Utilization of clinical technology and mobile health (mHealth) is expanding globally. It is important to reflect on how their usage and application could translate in low- and middle-income country (LMIC) settings. With the exponential growth and advancements of mobile and wireless technologies, LMICs are prime to adapt such technologies to potentially democratize and create solutions to health-related challenges. The role of these technologies in oncology clinical trials continues to expand. The lure of mHealth promises disruptive technology that may change the way clinical trials are designed and conducted in many settings. Its applicability in the African context is currently under consideration. Although potentially of expanding benefit, the role of these technologies requires careful and nuanced evaluation of the context in which they might be applied to harness their full potential, while mitigating possible harms or preventing further deepening of disparities within populations. Moreover, technology and digital innovations are no substitute for poor referral pathways and dysfunctional health systems and can only complement or enhance definite strategies aimed at strengthening these health systems.
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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".