AN ANALYSIS OF THE EFFECTIVENESS OF CURRENT PROVISION OF FATIGUE MANAGEMENT STRATEGIES WITHIN A NEURO-ONCOLOGY MDT CLINIC
Bibliographic record
Abstract
Abstract AIMS Cancer-related fatigue (CRF) is known to have a distressing impact on a patient’s function and quality of life, with as many as 9 out of 10 people with cancer (90%) experiencing CRF (Macmillan 2021). Management of CFR for patients with a Glioblastoma diagnosis is additionally complex given associated neurological affect. Physiotherapy and Occupational Therapy (PT & OT) have an important role addressing CRF through interventions such as, graded exercise programmes, fatigue education, self-management strategies, cognitive rehab and sleep hygiene advice. Intervention varies depending on when the patient is being referred or logistics of the clinic setting. METHOD A 12-month review of the OT & PT caseload (January 2022 – December 2022) is under evaluation to establish the number of patients reporting CRF at initial assessment or at follow up review. Clinic notes of patients reporting CRF have been reviewed to determine the oncological treatment time point that CRF was identified – pre, mid or post treatment. Data collection also includes patient and clinical characteristics, and the rehabilitation intervention type offered correlated to treatment phase. RESULTS 49 datasets are under evaluation, with the key aims to explore 1) the prevalence of CRF in patients referred to the Cancer Specialist OT and PT team, 2) correlation of reporting’s of CRF with treatment phase 3) the Rehabilitation Treatment Taxonomy deployed in the management of CRF. CONCLUSIONS Our evaluation will explore patients CRF needs and how they may change through the treatment pathway, leading to future recommendations for service quality improvement and implementation change.
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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 | 0.006 | 0.035 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".