An optimization model for equitable accessibility to magnetic resonance imaging technology in developing countries
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) is a sophisticated and costly technology that provides highly accurate diagnoses of various medical conditions using a powerful magnetic field, radiofrequency pulses, and a computer to produce detailed pictures of internal body parts and organs. The dissemination of MRI use at medium and high-complexity healthcare facilities increases the cost of healthcare systems. It imposes accessibility challenges concerning the equitable availability of essential healthcare technology in developing countries. Despite the importance of this technology, very few studies approach this problem from multiple location–allocation perspectives. We propose an optimization model for equitable accessibility to MRI technology. We study this problem for the Brazilian National Health System at the municipality level, and recommend alternative locations, and acquire the new devices and technologies equitably throughout the country and health system. We show that while some municipalities have an oversupply, several regions in the country have no access to MRI technology. The models propose the number of new MRIs and their locations for needing municipalities considering equity principles. The results show, for instance, that the acquisition of 210 MRIs is enough to satisfy 95% of the demand for such service, with patients traveling 44 km on average in northern Brazil. We report accessibility gains from adopting the location–allocation plans developed using the optimization model proposed in this study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".