SynthesisJournal of cancer research and clinical oncology2025
Prediction models for different types of leukemia: a systematic review and critical appraisal.
Synthesis in Journal of cancer research and clinical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Trial
- Extracellular Vesicles Associated Metabolites as Intercellular Signalling Mediators in Disease and Therapy.Metabolites · 2026Review
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
objectivesTo systematically review and evaluate the methodological quality and risk of bias (ROB) of leukemia prediction models essential for clinical decision-making.
methodsWe reviewed 148 prediction models published before August 2023 from PubMed, Embase, Cochrane Library, and Web of science databases. Two reviewers independently screened articles and extracted data using CHARMS criteria. ROB was assessed using PROBAST. Models were categorized by leukemia subtype and analyzed for methodological characteristics.
resultsA total of 61 acute myeloid leukemia (AML) models primarily predicted survival (82.0%), diagnosis (4.9%), or death (4.9%) using predictors including age, cytogenetic risk, and white blood cell count. Among the 22 chronic myeloid leukemia (CML) models, the focus was on survival (72.7%) and time to treatment (19.0%), utilizing blast percentage, age, and platelet count. A total of 21 chronic lymphocytic leukemia (CLL) models primarily predicted survival (71.4%) using IGHV status, Rai stage, and age. The methodological shortcomings including incomplete reporting, methodological limitations, and high ROB were consistent across different leukemia subtypes. Traditional statistical methods predominated (Cox regression 72.9%, logistic regression 12.2%), with only nine machine learning models. Critical methodological limitations included lack of internal validation (52.0%) and external validation (57.4%). Only 43.2% reported discrimination metrics (AUC 0.60-0.99), with 28.0% achieving AUC > 0.7. Calibration was reported in only 23.0% of models. High ROB affected 93.9% of studies, primarily due to inadequate data handling and validation.
conclusionsExisting leukemia prediction models have limited clinical utility due to methodological shortcomings and high ROB. Future research should prioritize transparent reporting, rigorous validation, and external validation to enhance clinical applicability and generalizability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.