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Record W4385411155 · doi:10.1097/ncc.0000000000001261

Stability of Symptom Clusters in Children With Acute Lymphoblastic Leukemia Undergoing Chemotherapy

2023· article· en· W4385411155 on OpenAlexaff
Rongrong Li, Xinyi Shen, Qi Yang, Lin Zhang, Huiling Li, Wenying Yao, Yuying Chan

Bibliographic record

VenueCancer Nursing · 2023
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsEmergent BioSolutions (Canada)
Fundersnot available
KeywordsMedicineCluster (spacecraft)DistressLymphoblastic LeukemiaChemotherapyEmotional distressInduction chemotherapyAcute lymphocytic leukemiaPediatricsPhysical therapyInternal medicineLeukemiaAnxietyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Children with acute lymphoblastic leukemia (ALL) experience multiple symptoms during chemotherapy. Assessing how symptoms cluster together and how these symptom clusters (SCs) change over time may lay a foundation for future research in SC management and the pathophysiological mechanisms of SCs. OBJECTIVES: This study aimed to assess the stability of SCs in children with ALL during chemotherapy. METHODS: A longitudinal investigation was carried out. The Chinese version of the Memorial Symptom Assessment Scale 10 to 18 was used to assess the occurrence, severity, and distress of symptoms in 134 children with ALL (8-16 years old) at the following 4 separate points: before chemotherapy (T1), start of post-induction therapy (T2), 4 months post-induction therapy (T3), and start of maintenance therapy (T4). Exploratory factor analyses were used to extract SCs. RESULTS: Six SCs were identified. Emotional and somatic clusters were identified across all dimensions and time points. Gastrointestinal cluster was all identified except for occurrence at T1. Neurological cluster was identified at T2 and T3 for all dimensions and at T4 for severity and distress. Self-image disorder cluster was all identified except at T1. Skin mucosa cluster was identified at T2 and T3 for all dimensions. Emotional cluster exhibited common symptoms across dimensions and time points. CONCLUSION: The number and types of SCs determined by scoring the occurrence, severity, and distress are different, but some SCs are relatively stable. IMPLICATIONS FOR PRACTICE: Clinicians should not only focus on the common trajectory of symptoms and SCs, but also assess each child individually.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.014
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.312
Teacher spread0.292 · 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.

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

Citations8
Published2023
Admission routes1
Has abstractyes

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