Assessing the utilization of cancer medicines in Rwanda: an analysis of treatment patterns
Bibliographic record
Abstract
Introduction: Cancer is a growing public health concern in Africa, especially in low- and middle-income countries (LMICs) like Rwanda. Increased cancer incidences translate into increased utilisation of cancer medicine. Access to affordable cancer medicines in Rwanda is a pressing issue as the National Health Insurance plan does not provide coverage for cancer medicines. In this study, we investigated the utilisation patterns of cancer medicines in Rwanda. Methods: = 3) capable of delivering chemotherapy in Rwanda. The data collection was over a period of 6 months, during which a team of trained research assistants reviewed a convenience sample of selected patient charts. Both paper charts and electronic medical records were used to collect patients' data, including cancer type, stage, treatment setting, type of drugs or regimen used and completed cycles. Data were analysed using descriptive statistics. Results: = 303). Thirty-six percent (221/630) had stage III cancer. The most common regimens within the cohort were adriamycin, cyclophosphamide and taxane, capecitabine and oxaliplatin (CAPOX), paclitaxel + carboplatin and a single agent cisplatin given concurrently with radiotherapy. The proportion of chemotherapy that was given in the curative and palliative setting was 72% and 28% respectively. Conclusion: Access to affordable cancer medicines remains a challenge in Rwanda. The study's findings provide valuable information on the utilisation patterns of cancer medicines in Rwanda, which can be used to guide policy decisions and improve cancer care in the country.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".