A pilot study to evaluate the feasibility of individualized yoga for inpatient children receiving intensive chemotherapy
Bibliographic record
Abstract
BACKGROUND: Fatigue is an important problem in paediatric cancer patients and yoga may be an effective intervention. The primary objective was to determine the feasibility of individualized yoga for hospitalized children receiving intensive chemotherapy. METHODS: We included English-speaking children and adolescents aged 7-18 years receiving intensive chemotherapy or haematopoietic stem cell transplantation (HSCT). Yoga was conducted three times weekly for three weeks. The primary outcome was feasibility, defined as ability to deliver at least 60% of planned sessions. Secondary outcomes were parent-reported Pediatric Quality of Life Inventory (PedsQL) Multidimensional Fatigue Scale, Fatigue Scale-Parent, PedsQL Generic Core Scales and PedsQL Acute Cancer Module. RESULTS: Between January and October 2013, 11 patients were enrolled. Median age was 14.0 (range 7.7-16.4) years and 6 (55%) were boys. Yoga was feasible with 10/11 participants meeting the threshold for feasibility. The median number of yoga sessions was 9 (range 3-13). No adverse events were attributed to yoga. Mean±standard deviation for the day 21 proxy-reported PedsQL general fatigue scores was 55.6±15.5. Qualitative comments suggested design changes for future yoga studies. CONCLUSIONS: Individualized yoga is feasible for inpatient children receiving intensive chemotherapy. Future work will include development and conduct of a randomized trial for fatigue amelioration. TRIAL REGISTRATION: ClinicalTrials.gov NCT02105389.
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 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".