Yoga for Pain and Anxiety in Pediatric Hematology- Oncology Patients: Case Series and Review of the Literature
Bibliographic record
Abstract
In response to rising interests from patients, many comprehensive cancer centers now offer an integrative model of care which includes the provision of complementary medicine. Yoga is one such intervention that has been used in children. We performed an Institutional Review Board (IRB) approved chart review of pediatric hematology-oncology patients who received yoga. Patients with sickle cell vaso-occlusive pain crises or cancer were offered bedside yoga by a certifi ed yoga instructor. Pain before and after yoga was reported using the 10-point Wong-Baker FACES pain scale. Anxiety before and after yoga was assessed with the State-Trait Anxiety Inventory. Qualitative patient quotes were extracted from the yoga therapist’s notes. Twenty patient’s charts were reviewed, 50% were male. The mean age was 18 years, (range 11–26 years.) Most common diagnoses were sickle cell vaso-occlusive pain crises (45%), and sarcoma (40%.) The mean reduction in pain score was 0.94 (SD ± 1.68), p = .005, and the mean reduction in anxiety T-score was 9.62 (SD ± 6.74), p = .0001. Patient quotes suggest that yoga was benefi cial, especially for relaxation. Literature review offers preliminary support for the use of yoga for anxiety in children. Our data suggest that further research on yoga as an effective intervention for pediatric hematology-oncology patients for pain and anxiety is needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".