Delay in the Diagnosis of Pediatric Brain Tumors in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Vague symptoms and a lack of pathognomonic features hinder the timely diagnosis of pediatric brain tumors (PBTs). However, patients in low- and middle-income countries (LMICs) must also bear the brunt of a multitude of additional factors contributing to diagnostic delays and subsequently affecting survival. Therefore, this study aims to assess these factors and quantify the durations associated with diagnostic delays for PBTs in LMICs. METHODS: A systematic review of extant literature regarding children from LMICs diagnosed with brain tumors was conducted. Articles published before June 2023 were identified using PubMed, Google Scholar, Scopus, Embase, Cumulative Index to Nursing and Allied Health Literature, and Web of Science. A meta-analysis was conducted using a random-effects model through R Statistical Software. Quality was assessed using the Newcastle Ottawa Scale. RESULTS: A total of 40 studies including 2483 patients with PBT from 21 LMICs were identified. Overall, nonspecific symptoms (62.5%) and socioeconomic status (45.0%) were the most frequently reported factors contributing to diagnostic delays. Potential sources of patient-associated delay included lack of parental awareness (45.0%) and financial constraints (42.5%). Factors contributing to health care system delays included misdiagnoses (42.5%) and improper referrals (32.5%). A pooled mean prediagnostic symptomatic interval was calculated to be 230.77 days (127.58-333.96), the patient-associated delay was 146.02 days (16.47-275.57), and the health care system delay was 225.05 days (-64.79 to 514.89). CONCLUSION: A multitude of factors contribute to diagnostic delays in LMICs. The disproportionate effect of these factors is demonstrated by the long interval between symptom onset and the definitive diagnosis of PBTs in LMICs, when compared with high-income countries. While evidence-based policy recommendations may improve the pace of diagnosis, policy makers will need to be cognizant of the unique challenges patients and health care systems face 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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".