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Record W2130798268 · doi:10.3109/10428194.2012.664843

Association between obesity at diagnosis and weight change during induction and survival in pediatric acute lymphoblastic leukemia

2012· article· en· W2130798268 on OpenAlexafffund
Marie‐Chantal Ethier, Sarah Alexander, Oussama Abla, Gloria Green, Renita Lam, Lillian Sung

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2012
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick Children
FundersCanadian Institutes of Health Research
KeywordsMedicineHazard ratioConfidence intervalInternal medicineUnivariate analysisObesityWeight changeWeight gainGastroenterologyWeight lossBody weightMultivariate analysis

Abstract

fetched live from OpenAlex

For children with acute lymphoblastic leukemia (ALL), the impact of obesity at diagnosis and weight change during induction on survival is uncertain. Objectives of this study were to describe the relationship between obesity and weight change during induction and event-free survival (EFS) and overall survival (OS). Participants were children 2-18 years old with ALL diagnosed between January 2001 and September 2006. Univariate and multiple regression analyses were conducted. In total 238 children were included; 21 (8.8%) were obese at diagnosis. Obese patients, compared with non-obese patients, had lower 5-year EFS (62.2±12.1% vs. 83.6±2.6%; p =0.02) and OS (80.7±8.7% vs. 92.9±1.9%; p =0.005). In univariate analysis, weight gain during induction was associated with better EFS (hazard ratio [HR] =0.89, 95% confidence interval [CI] 0.82-0.97; p =0.009) and OS (HR =0.81, 95% CI 0.74-0.90; p <0.0001). Obese pediatric patients with ALL have inferior survival while increased weight during induction may be associated with better survival. Causes of weight loss during induction should be aggressively managed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.260
Teacher spread0.239 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations32
Published2012
Admission routes2
Has abstractyes

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