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The Use of Complementary and Alternative Medicine in Children with Chronic Medical Conditions

2006· article· en· W2106577647 on OpenAlexaff
Dawa Samdup, Ronald G. Smith, Soon Il Song

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2006
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineMassageDiabetes mellitusPhysical therapyDiseaseAlternative medicineCerebral palsyCystic fibrosisPediatricsFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: The objectives of this study were to examine whether progressive medical conditions lead to greater use of complementary and alternative medicine (CAM) as compared with more stable conditions, to see whether disease severity influences CAM use, and to identify the main motivations behind CAM use. METHODS: Subjects were selected from outpatient clinics at Hotel Dieu Hospital. Surveys were conducted by mail and telephone. Medical diagnosis and severity were obtained from medical files. Statistical tests included chi, Kruskal-Wallis, and correlations. RESULTS: One hundred ninety-four children were surveyed. The "progressive" group included 15 patients with Duchenne muscular dystrophy and 22 patients with cystic fibrosis. The "nonprogressive" group included 85 patients with cerebral palsy (CP), 49 with diabetes mellitus, and 23 with spina bifida. Twenty-three percent were using CAM. CP had the highest use; diabetes mellitus had the lowest. Popular therapies included massage and dietary/herbal remedies. Progressiveness had no impact on CAM use. Within the CP group, greater disease severity was associated with higher use (P < 0.001). The main reason for CAM use was to complement conventional medicine. CONCLUSIONS: Disease progressiveness had no impact on CAM use, but severity within the CP group did. Complementing conventional medicine was the main motive. Understanding the reasons and patterns of use of CAM is beneficial in efforts to improve the care of children with chronic medical conditions.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
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 head0.016
GPT teacher head0.330
Teacher spread0.314 · 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

Citations67
Published2006
Admission routes1
Has abstractyes

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