Challenges of Urologic Oncology in Low-to-Middle-Income Countries
Bibliographic record
Abstract
We performed a literature review to identify articles regarding the state of urological cancers in low-to-middle-income countries (LMICs). The challenges that LMICs face are multifactorial and can include poor health education, inadequate screening, as well as limited access to treatment options and trained urologists. Many of the gold standard treatments in high-income countries (HICs) are scarce in LMICs due to their poor socioeconomic status, leading to an advanced stage of disease at diagnosis and, ultimately, a higher mortality rate. These standards of care are vital components of oncological disease management; however, the current and sparse literature available from LMICs indicates that there are many obstacles delaying early diagnosis and management options in LMICs. In the era of evolving medical diagnosis and treatments, sufficient data must be gathered and understood in order to provide appropriate diagnostic and treatment options to curtail rising mortality rates and, therefore, help to alleviate the burden in LMICs.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".