Survival Outcomes for Adolescent and Young Adults With Cancer in Low- and Middle-Income Countries: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Patients with adolescent and young adult (AYA) cancer are recognized as a vulnerable subpopulation in high-income countries (HICs). Although survival gaps between HIC and low- and middle-income country (LMIC) children with cancer are well described, LMIC AYAs have been neglected. We conducted a systematic review to describe cancer outcomes among LMIC AYAs. METHODS: We captured English language studies published from 2010 onward reporting LMIC AYA cancer survival outcomes. LMICs were defined according to World Bank 2019 classifications, whereas AYAs were defined as diagnosed between age 15 and 39 years. Cohorts were considered AYA if >75% of patients were AYA, the mean/median age and standard deviation were between 15 and 39 years, or the range was within 5 years of the AYA range (ie, 10-45 years). Cohort characteristics were abstracted, including country, cancer type, and cancer outcomes. RESULTS: Of 6,207 studies identified by the search strategy, 658 underwent full-text review; 60 met inclusion criteria. No low-income countries were represented. Forty-four (73.3%) studies were conducted in upper-middle-income countries (UMICs) although these represented only 12 of 55 countries currently classified as UMICs. The most common cancers studied were acute lymphoblastic leukemia (n = 13 studies), breast cancer (n = 5), and osteosarcoma (n = 3). Five-year overall survival was highly variable, ranging from 39% to 63% for ALL, 60%-85% for breast cancer, and 47%-83% for osteosarcoma. CONCLUSION: Although three billion AYAs reside in LMICs, their cancer outcomes are neglected in the current literature. Existing data indicate variable survival, ranging from comparable with HIC outcomes to substantially inferior. These studies, however, represent only a limited number of LMICs and are biased toward UMICs. Systematic efforts to describe and improve LMIC AYA cancer outcomes are required.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".