Analysis of the Gap in PCR Monitoring Availability for Patients with Chronic Myeloid Leukemia in 60 Low- and Middle-Income Countries
Bibliographic record
Abstract
Abstract Purpose: To estimate the resource gap in the polymerase chain reaction (PCR) monitoring for patients with chronic myeloid leukemia (CML) in low- and middle-income countries (LMICs). Methods: We developed a model of demand and supply of PCR monitoring of CML patients in 60 LMICs. PCR testing was assumed to use Cepheid’s GeneXpert® IV system. We included costs of GeneXpert® instruments, uninterrupted power supplies, warranties, calibration kits, test cartridges, and shipping. We calculated the country-specific monetary gap in PCR monitoring, stratified by country priority defined as the availability of tyrosine kinase inhibitors (TKIs) through The Max Foundation initiatives. Results: The five-year gap in PCR monitoring was $29.1 million across all countries, 22% ($6.4 million) in countries with all five TKIs available, 20% ($5.7 million) in countries with four TKIs available, 50% ($14.5 million) in countries with three TKIs available, 8% ($2.2 million) in countries with two TKIs available, and 1% ($0.3 million) in countries with one TKI available. The gap was highest in South Asia (52%; $15.1 million) and lowest in Latin America (6%; $1.9 million). Excluding labor costs, the bulk of the resource needs (86%; $25.2 million) were for procurement of BCR-ABL cartridges. Conclusion: Removing the five-year gap in PCR monitoring capacity for CML in LMICs will require the mobilization of significant resources and will likely lead to better treatment outcomes and reduced treatment costs through optimization of treatment, discontinuation of therapy in appropriate patients, and facilitation of clinical research. Development of streamlined monitoring guidelines for resource-limited countries should be considered.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".