Development of a Novel Massage Therapy Outcome Measure for Children and Young Adults Receiving Hematopoietic Cell Transplant
Bibliographic record
Abstract
Background: Children receiving hematopoietic stem cell transplantation (HCT) often experience an unfortunate sequalae of negative effects including pain, deconditioning, and anxiety. Massage therapy (MT) has demonstrated effective non-pharmacological management of fatigue, pain, and anxiety in patients undergoing cancer treatment. Existing studies have been limited by the lack of available MT-specific outcome measures to track responses to interventions. Purpose: This study aimed to describe the creation of a novel MT-specific outcome measure to be utilized in the pediatric acute-care setting and establish construct validity for this measure to assess clinical effectiveness of MT interventions. Setting: An oncology ward at a large pediatric tertiary medical center in the United States. Participants: A total of 58 children and young adults undergoing HCT. Research Design: Retrospective Cohort Study. Intervention: A panel of massage therapists created a novel outcome measure, OMPREP, for use in MT sessions and performed a literature review to ensure face validity of the tool. This outcome measure was administered to patients and data were collected retrospectively to assess construct validity. Results: <.001) scores were all significantly greater at evaluation and discharge compared to the lowest observed scores post-HCT. Conclusion: The novel MT-specific outcome measure, OMPREP, was feasible and demonstrated construct validity when implemented in a pediatric acute-care setting by massage therapists. This new tool may offer a quantitative measure of MT-interventions and assist in tracking patient outcomes.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".