Does Down syndrome affect the prognosis of acute lymphoblastic leukemia? A systematic review and meta-analysis
Bibliographic record
Abstract
Down Syndrome (DS) is the most common human aneuploid abnormality. However, no association has been established between the presence of DS in Acute Lymphoblastic Leukemia (ALL) patients to affect survival rates. We searched in Medline, Scopus, and Web of Science databases for relevant studies. Binary outcomes were evaluated using risk ratios (RRs) with 95% confidence intervals (CIs). The risk of bias was performed using the Newcastle – Ottawa Scale (NOS). We included randomized controlled trials and cohort studies, with patients with newly diagnosed ALL and excluded studies with overlapping, reviews, animal model studies, or letters. All endpoints were analyzed using random-effect models. Heterogeneity was assessed using I2 statistics. Statistical analyses were performed using R, version 4.2.3. Sixteen studies, comprising 63,054 patients, were included. The outcomes demonstrated a significant difference favoring the ALL group for the outcomes as induction failure (RR 5.51 95% CI 3.50–8.69; p < 0.001); treatment-related mortality (RR 4.29 95% CI 3.38–5.45; p < 0.001), and total relapse (RR 1.28 95% CI 1.08–1.53; p = 0.004). There was no significant difference between the groups for event-free survival (5 years) groups (RR 0.91 95% CI 0.74–1.13; p = 0.40) and central nervous system relapse (RR 0.98 95% CI 0.55–1.72; p = 0.93). This meta-analysis found a significant difference when comparing the DS-ALL group with the ALL group, the results supported a higher risk of induction failure, treatment-related mortality, and total relapse in the DS-ALL group. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO), National Institute for Health and Care Research (NIHR), with registration number CRD5689342.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.004 | 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".