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Record W2347087085 · doi:10.1177/1534735416630806

Development of an Individualized Yoga Intervention to Address Fatigue in Hospitalized Children Undergoing Intensive Chemotherapy

2016· article· en· W2347087085 on OpenAlexaff
Caroline Diorio, Amanda Celis Ekstrand, Tanya Hesser, Cathy O’Sullivan, Michelle Lee, Tal Schechter, Lillian Sung

Bibliographic record

VenueIntegrative Cancer Therapies · 2016
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHospital for Sick ChildrenMcMaster University
Fundersnot available
KeywordsMedicineIntervention (counseling)Physical therapySession (web analytics)Flexibility (engineering)Clinical trialQuality of life (healthcare)Nursing

Abstract

fetched live from OpenAlex

Purpose Fatigue is an important problem in children receiving intensive chemotherapy and hematopoietic stem cell transplantation (HSCT). Exercise may be an effective intervention for fatigue. Individualized yoga represents an ideal intervention because it can be tailored according to an individual child's needs. Little is known about how to structure a standardized yoga program for intensivelytreated children. Therefore, this study describes the development of a yoga program and an approach to monitoring sessions suitable for hospitalized children receiving intensive chemotherapy or HSCT. Methods The yoga program was designed to increase mobility in hospitalized children and to provide children with relaxation techniques that could be used independently in a variety of environments. The program was founded on 4 key tenets: safety, adaptability, environmental flexibility, and appeal to children. We also developed quality and consistency assurance procedures. Results A menu format with a fixed structure was selected for the yoga program. Each yoga session contained up to 6 sections: breathing exercises, warmup exercises, yoga poses, balancing poses, cool-down poses, and final relaxation. Yoga instructors selected specific yoga poses for each session from a predetermined list organized by intensity level (low, moderate, or high). Monitoring procedures were developed using videotaping and multirater adjudication. Conclusion We created a standardized yoga program and an approach to monitoring that are now ready for incorporation in clinical trials. Future work should include the adaptation of the program to different pediatric populations and clinical settings.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.368
Teacher spread0.324 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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