Value of Geographical Information Systems in Analyzing Geographic Accessibility to Inform Radiotherapy Planning: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Vulnerable populations face geographical barriers in accessing radiotherapy (RT) facilities, resulting in heterogeneity of care received and cancer burden faced. We aimed to explore the current use of Geographical Information Systems (GIS) in access to RT and use these findings to create sustainable solutions against barriers for access in low- and middle-income countries. MATERIALS AND METHODS: A systematic review using the PRISMA search strategy was done for studies using GIS to explore outcomes among patients with cancer. Included studies were reviewed and classified into three umbrella categories of how GIS has been used in studying access to RT. RESULTS: Forty articles were included in the final review. Thirty-eight articles were set in high-income countries and two in upper-middle-income countries. Included studies were published from 2000 to 2020, and were comprised of patients with all-cancers combined, breast, colon, skin, lung, prostate, ovarian, and rectal carcinoma patients. Studies were categorized under three groups on the basis of how they used GIS in their analyses: to describe geographic access to RT, to associate geographic access to RT with outcomes, and for RT planning. Most studies fell under multiple categories. CONCLUSION: Although this field is relative nascent, there is a wide array of functions possible through GIS for RT planning, including identifying high-risk populations, improving access in high-need areas, and providing valuable information for future resource allocation. GIS should be incorporated in future studies, especially set in low- and middle-income countries, which evaluate access to RT.
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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 | 0.024 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.028 | 0.037 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".