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Record W2099220112 · doi:10.3109/10428194.2011.639018

Non-relapse mortality in pediatric acute lymphoblastic leukemia: a systematic review and meta-analysis

2011· review· en· W2099220112 on OpenAlexafffund
Esther Blanco, Joseph Beyene, Anne Marie Maloney, Rowena Almeida, Marie‐Chantal Ethier, Naomi Winick, Sarah Alexander, Lillian Sung

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2011
Typereview
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsMcMaster UniversityInstitute for Clinical Evaluative SciencesUniversity of TorontoSickKids FoundationPublic Health OntarioHospital for Sick Children
FundersCanadian Institutes of Health Research
KeywordsMedicineLymphoblastic LeukemiaMeta-analysisInternal medicineComplete remissionPediatricsMortality rateLeukemiaChemotherapy

Abstract

fetched live from OpenAlex

The primary objective of the study was to describe non-relapse mortality (NRM) and the proportion of first events that are deaths in children with acute lymphoblastic leukemia (ALL). Secondary objectives were to identify groups at higher risk and to determine whether proportions have changed over time. We performed a systematic review of randomized pediatric ALL studies. From 1337 articles, 59 were included, comprising a total of 49 071 patients. The induction death rate was 1.38%, remission death rate was 1.94% and total NRM was 3.60%. Deaths were responsible for 53.84% of first events during induction and 13.03% in total. Standard risk patients had significantly lower NRM during remission. The year of study was not associated with NRM. The results of the study show that the rate of NRM in children with ALL is 3.60% and those with high risk ALL have significantly higher NRM during remission, but NRM has not changed over time. Future research should focus on the exploration of patient-related risk factors for NRM.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.762
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0350.008
Bibliometrics0.0050.009
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0040.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.047
GPT teacher head0.325
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations35
Published2011
Admission routes2
Has abstractyes

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