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Record W4404690891 · doi:10.1089/jpm.2024.0194

Impact of Baduanjin Qigong Exercise on Fatigue in Patients with Lung Cancer: A Randomized Controlled Trial

2024· article· en· W4404690891 on OpenAlexaboutno aff
Yirui Liu, Xinjun Liang, Bin Yang, Yuan Wu, Qian Yu

Bibliographic record

VenueJournal of Palliative Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicBiofield Effects and Biophysics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRandomized controlled trialCancer-related fatiguePhysical therapyLung cancerExercise therapyCancerInternal medicine

Abstract

fetched live from OpenAlex

Background:Patients report fatigue as the most distressing symptom associated with cancer and treatment. Baduanjin has beneficial effects on reducing fatigue. However, no relevant randomized controlled trials comparing the effects of Baduanjin exercise with routine exercise in patients with lung cancer and fatigue have been reported. Methods:This blinded trial aimed to compare the effect of Baduanjin versus routine exercise on fatigue for patients with lung cancer. Participants in the intervention group received Baduanjin training and performed Baduanjin every week, while those in the control group performed routine exercise at the same frequency. Results:A total of 73 patients were analyzed. After the intervention, patients in the Baduanjin group experienced significant improvement in fatigue and pain (p < 0.05), while no significant difference in Edmonton Symptom Assessment System items were observed among patients in the exercise group. Conclusion:Our study showed that Baduanjin was a better exercise than routine activity in relieving their fatigue.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.353
Teacher spread0.337 · 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 designRandomized trial
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

Citations4
Published2024
Admission routes1
Has abstractyes

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