The survival of patients enrolled in a global direct-to-patient cancer medicine donation program: The Glivec International Patient Assistance Program (GIPAP)
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The Glivec International Patient Assistance Program (GIPAP) is a unique direct-to-patient program that provides imatinib (Glivec) at no cost to eligible patients in low- and middle-income countries (LMICs) with chronic myelogenous leukemia (CML) or gastrointestinal stromal tumor (GIST). This paper analyses the output, outcome and impact of the program between 2001 and 2014 using the data collected by the Max Foundation. METHOD: We extracted data on GIPAP patients' country of residence, sex, diagnosis, date of enrollment in GIPAP, age at enrollment, case closure date, and reason for closure from The Max Foundation database covering the period 2001 to 2014. We used Kaplan-Meier method to assess the survival rate of patients in GIPAP and used the proportional hazard regression model to estimate the effect of different variables on patients' survival. FINDINGS: About 63,000 GIPAP patients in 93 countries received over 71 million defined daily doses (DDD) of imatinib between 2001 and 2014. Our analysis showed that GIPAP patients had a 5-year survival rate of 89% which compares favorably to survival in high income countries despite the challenges of delivering cancer care in LMICs. Age at enrollment into the program, sex, duration between diagnosis and enrollment into program, year of enrollment, and patients' diagnosis (CML vs non-CML) were factors that influenced survival. INTERPRETATION: The GIPAP program has improved the survival of CML and GIST patients in LMICs, most of whom would not have had access to imatinib in the absence of the donation and therapeutic support of the program. FUNDING: This work was funded as part of Access Accelerated case studies. Access Accelerated is an initiative of more than 20 global biopharmaceutical companies in partnership with the World Bank and Union of International Cancer Control that seeks to reduce barriers to prevention, treatment and care for non-communicable diseases in LMICs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it