Why is it Important to Identify Residual Cells in Leukaemia Patients after Treatment? A Review Article
Bibliographic record
Abstract
Introduction: Most children diagnosed with acute lymphoblastic leukemia are cured and are not at risk of relapse. However, 20% of children are at a high risk of experiencing relapse later on in their lives. In order to detect risk and obtain prognostic information, the quantification of minimal residual disease (MRD) can be utilized. Detection of MRD can lead to efficient identification of relapse risk. However, there is limited understanding of the association between MRD and long-term outcomes after treatment in children. Therefore, this systematic review will examine existing literature to determine the strength of association between MRD negativity and relapse risk in children and its importance in the prediction of relapse. Methods: A systematic review of 5 articles centered around ALL in children was analyzed to examine the strength of association between MRD negativity and clinical outcomes of event-free survival (EFS) and overall survival (OS) following the PRISMA guideline. The literature search was done through databases such as NCBI, PubMed, and other childhood oncology databases. The inclusion criteria included peer-reviewed clinical studies that focus on ALL relapse risk and MRD detection. Additionally, reviews, abstracts, and studies with inadequate sample sizes or correlations were excluded. Data were extracted and organized based on criteria of MRD negativity, MRD detection type, and relapse risk level. The data collected from all studies were analyzed through a meta-analysis. The five publications discussed in this article were a total of 11,265 participants. Results: Results: The results portion of your abstract should concisely describe a summary of the main findings. A positive correlation was determined between EFS and OS hazard ratios and MRD detection. Discussion: The analysis of the five publications demonstrated that MRD is an important marker and a strong predictor of relapse in children who are diagnosed with ALL. Conclusion: MRD detection can be proposed as a method of predicting a high risk of relapse in children with ALL. In essence, this literature review has the potential to identify the clinical and therapeutic significance of MRD testing which can be utilized to predict and prevent relapse of ALL in children.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".