The magnitude and predictors of therapy abandonment in pediatric central nervous system tumors in low‐ and middle‐income countries: Systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Outcomes of pediatric central nervous system (CNS) tumors in low‐ to middle‐income countries (LMIC) are poorer than their high‐income counterparts. Abandonment of therapy is increasingly recognized as a key contributor to this disparity, but has been poorly quantified. We performed a meta‐analysis to determine the magnitude of abandonment in pediatric CNS tumors in LMIC, and risk factors and interventions aimed at reducing this. Patients and methods We searched seven databases for pediatric CNS tumor cohorts followed up from diagnosis and treated in LMIC. All languages were included. Two reviewers independently selected articles and extracted data on abandonment rates (ARs) and predictors. The authors were contacted for additional information. Results Of 50 660 publications, 643 in five languages met criteria for full review; 131 met analysis inclusion criteria. ARs were not reported in the majority, and a small number were available from the authors. Available ARs ranged from 0% to 59%, from 38 studies (2497 children in 14 countries), and these were quantitatively analyzed. Lower‐middle‐income countries had higher ARs than upper‐middle‐income countries (27%, 95% confidence interval [CI] 20%–36% vs 9%, 95% CI 6%–14%, P < 0.0001), with significant heterogeneity within each (LMIC I2 = 78%, P < 0.00001, UMIC I2 = 85%, P < 0.00001). Common predictors for abandonment included distance to treatment centers, financial hardship, and prognostic misconceptions. Conclusion In LMICs, ARs are highest in lower‐MICs. However, the paucity of published data limits further evaluation. Given the increasing burden of pediatric CNS tumors in LMIC, addressing deficits in abandonment reporting is critical. Consistent reporting is needed for developing interventions to improve outcomes.
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".