WHAT ROLE CAN A PERSONAL TRAINER PLAY IN IMPROVING QUALITY OF LIFE THROUGHOUT ONCOLOGY TREATMENT FOR PEOPLE WITH A BRAIN TUMOUR
Bibliographic record
Abstract
Abstract AIMS Review of a single person case study to establish if a personal trainer (PT) could help build a personalised pro- gramme for people with brain tumours going through oncology treatment. The aim being to help manage resilience both physically and psychologically through treatment and improve quality of life. METHOD A young person who has completed radiotherapy and chemotherapy after surgical debulking of a brain tumour was contacted. He worked as a PT before diagnosis and continued throughout treatment. He gradually increased his exercise tolerance and tailored exercises to different days of the treatment cycle to match his needs. He managed his fatigue and gradually built his exercise tolerance up back to baseline and continue with treatment. RESULTS This patient created a ‘patient guide’ with suggested exercises from his unique perspective as a person who has been through treatment and as a PT. CONCLUSIONS This guide has not yet been trialled with the rest of the patient population. The hope is that this will be used to guide further research in this field and may lead to the implementation of a permeant PT member of staff to the oncology service. In his words ‘this is just an example of my journey and how I stumbled my way through the diffculty of a cancer diagnosis and subsequent treatment. If you can take something away and apply it to your own situation then that’s brilliant’, ‘each day is a testament to how well you’re doing’
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".