Barriers to effective treatment of pediatric solid tumors in middle‐income countries: Can we make sense of the spectrum of nonbiologic factors that influence outcomes?
Bibliographic record
Abstract
BACKGROUND: The delivery of effective treatment for pediatric solid tumors poses a particular challenge to centers in middle-income countries (MICs) that already are vigorously addressing pediatric cancer. The objective of this study was to improve the current understanding of barriers to effective treatment of pediatric solid tumors in MICs. METHODS: An ecologic model centered on pediatric sarcoma and expanded to country as the environment was used as a benchmark for studying the delivery of solid tumor care in MICs. Data on resources were gathered from 7 centers that were members of the Central American Association of Pediatric Hematologists and Oncologists (AHOPCA) using an infrastructure assessment tool. Pediatric sarcoma outcomes data were available, were retrieved from hospital-based cancer registries for 6 of the 7 centers, and were analyzed by country. Patients who were diagnosed from January 1, 2000 to December 31, 2009 with osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, and other soft tissue sarcomas were included in the analysis. To explore correlations between resources and outcomes, a pilot performance index was created. RESULTS: The analyses identified specific deficits in human resources, communication, quality, and infrastructure. The treatment abandonment rate, the proportion of metastatic disease at diagnosis, the relapse rate, and the 4-year abandonment-sensitive overall survival (AOS) rate varied considerably by country, ranging from 1% to 38%, from 15% to 54%, from 24% to 52%, and from 21% to 51%, respectively. The treatment abandonment rate correlated inversely with health economic expenditure per capita (r = -0.86; P = .03) and life expectancy at birth (r = -0.93; P = .007). The 4-year AOS rate correlated inversely with the mortality rate among children aged <5 years (r = -0.80; P = 0.05) and correlated directly with the pilot performance index (r = 0.98; P = 0.005). CONCLUSIONS: Initiatives to improve the effectiveness of treatment for pediatric solid tumors in MICs are warranted, particularly for pediatric sarcomas. Building capacity and infrastructure, improving supportive care and communication, and fostering comprehensive, multidisciplinary teams are identified as keystones in Central America. A measure that meaningfully describes performance in delivering pediatric cancer care is feasible and needed to advance comparative, prospective analysis of pediatric cancer care and to define resource clusters internationally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".