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Record W2910571611 · doi:10.1182/blood-2018-99-112152

Quality of Life in Pediatric Acute Myeloid Leukemia: A Report from the Children's Oncology Group

2018· article· en· W2910571611 on OpenAlexaff
Rajaram Nagarajan, Todd A. Alonzo, Robert B. Gerbing, Donna L. Johnston, Richard Aplenc, E. Anders Kolb, Soheil Meshinchi, Lamia P. Barakat, Lillian Sung

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHospital for Sick ChildrenChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineInternal medicineCommon Terminology Criteria for Adverse EventsQuality of life (healthcare)Chemotherapy regimenAdverse effectHazard ratioOncologyPediatricsCancerConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background: Objectives were to describe guardian proxy-reported quality of life (QoL) during chemotherapy for pediatric acute myeloid leukemia (AML) and to identify treatment and demographic factors associated with worse QoL. Methods: Children's Oncology Group phase 3 AAML1031 study was a randomized trial for de novo AML patients age 0-30 to receive standard AML therapy with and without bortezomib. Patients with high risk FLT3-internal tandem duplication high allelic ratio (ITD HAR) were allocated to receive sorafenib in addition to the standard chemotherapy. Patients enrolled on the AAML1031 study who were 2-18 years of age at diagnosis with English or Spanish-speaking guardians were eligible to participate in the QoL portion of the study which included the PedsQL 4.0 Generic Core Scales, PedsQL 3.0 Acute Cancer Module and PedsQL Multidimensional Fatigue Scale. QoL assessments were obtained at four timepoints - at diagnosis and following completion of second, third and fourth (final) course of therapy. Guardians provided proxy assessments for all patients, while self-report for patients 5 years of age or older who could understand English was optional. This analysis focused on guardian proxy-reported QoL for patients who did not have FLT3-ITD HAR. In addition to demographic and treatment related factors, the total number of non-hematological grade 3-4 CTCAE (Common Terminology Criteria for Adverse Events) toxicities occurring during the time frame of QoL assessments was examined as a potential predictor of QoL. Results: There were a total of 4105 QoL submissions and there were 3513 non-hematological grade 3-4 CTCAE toxicities reported: 1339 submissions at diagnosis with 1088 toxicities reported, 1112 submissions following the second course with 721 toxicities, 929 submissions following third course with 911 toxicities, and 725 submissions following the fourth course with 793 toxicities. In repeated measures linear regression the number of submitted CTCAE toxicities was significantly associated with worse physical health (β±standard error (SE) -3.00±0.69; P<0.001) and general fatigue (β±SE -2.50±0.66; P<0.001). Older age was significantly associated with general fatigue (β±SE -0.58±0.25; P=0.022). In contrast, gender, risk status, bortezomib assignment, duration of neutropenia, private insurance status, white race and Hispanic ethnicity were not associated with physical health, psychosocial health or fatigue. Conclusions: The number of CTCAE toxicities was an important factor influencing physical QoL and fatigue among children on treatment for AML. Identifying novel approaches for reducing toxicities should be a priority to potentially improve QoL. Disclosures No relevant conflicts of interest to declare.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.322
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2018
Admission routes1
Has abstractyes

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