Interventions to improve early detection of childhood cancer in low‐ and middle‐income countries: A systematic review
Bibliographic record
Abstract
BACKGROUND: Childhood cancer outcomes in low- and middle-income countries (LMICs) lag behind those in high-income countries (HICs), in part due to late presentation and diagnosis. Though several interventions targeting early detection of childhood cancer have been implemented in LMICs, little is known about their efficacy. METHODS: We conducted a systematic review to identify studies describing such interventions. We searched multiple databases from inception to December 4, 2019. Studies were included if they reported on LMIC interventions focused on: (a) training of health care providers on early recognition of childhood cancer, or (ii) public awareness campaigns. We used preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines to conduct our review. The risk of bias in nonrandomized studies of interventions (ROBINS-I) checklist was used to assess quality of studies. RESULTS: Twelve studies met inclusion criteria (n = 5 full text, n = 7 abstract only). Five studies focused on retinoblastoma only, while the others focused on all types of childhood cancer. The majority studied multiple interventions of which early detection was one component, but reported overall outcomes. All identified studies used pre-post evaluative designs to measure efficacy. Five studies reported statistically significant results postintervention: decrease in extraocular spread of retinoblastoma, decrease in rates of refusal/abandonment of treatment, increase in number of new referrals, increase in knowledge, and an absolute increase in median 5-year survival. Other studies reported improvements without tests of statistical significance. Two studies reported no difference in survival postintervention. The ROBINS-I checklist indicated that all studies were at serious risk of bias. CONCLUSION: Though current evidence suggests that LMIC interventions targeting early detection of childhood cancer through health professional training and/or public awareness campaigns may be effective, this evidence is limited and of poor quality. Robust trials or quasi-experimental designs with long-term follow up are needed to identify the most effective interventions. Such studies will facilitate and inform the widespread uptake of early detection interventions across LMIC settings.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".