The burden of pediatric critical illness among pediatric oncology patients in low- and middle-income countries: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Pediatric oncology patients have increased risk for critical illness; outcomes are well described in high-income countries (HICs); however, data is limited for low- and middle-income countries (LMICs). METHODS: We systematically searched PubMed, EMBASE, Web of Science, CINAHL and Global Health databases for articles in 6 languages describing mortality in children with cancer admitted to intensive care units (ICUs) in LMICs. Two investigators independently assessed eligibility, data quality, and extracted data. We pooled ICU mortality estimates using random effect models. RESULTS: Of 3641 studies identified, 22 studies were included, covering 4803 ICU admissions. Overall pooled mortality was 30.3 % [95 % Confidence-interval (CI) 21.7-40.6 %]. Mechanical ventilation [odds ratio (OR) 12.2, 95 %CI:6.2-24.0, p-value<0.001] and vasoactive infusions [OR 6.3 95 %CI:3.3-11.9, p-value<0.001] were associated with ICU mortality. CONCLUSIONS: ICU mortality among pediatric oncology patients in LMICs is similar to that in HICs, however, this review likely underestimates true mortality due to underrepresentation of studies from low-income countries.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.008 | 0.010 |
| 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".