64 Are holistic needs assessments (HNA) being consistently implemented in adult cancer clinical practice?
Bibliographic record
Abstract
Introduction In the United Kingdom, over 12,000 adults a year are diagnosed with a brain tumour, accounting for 3% of all new cancer cases. The needs of patients with brain tumours are unique and complex. At present, patients with brain tumours have inconsistent assessment of their supportive care needs across care settings in Wales and the wider UK. Studies conducted in any adult cancer population which assess clinical implementation of a Holistic Needs Assessment (HNA) may provide evidence applicable to the brain tumour population. Aims To identify any HNAs of relevance to the care of those with brain tumours, we sought to examine clinical implementation of HNAs in any adult cancer population. Method Five databases were searched using text words and medical subject headings from 2008 to March 2023. Reference lists of systematic reviews were checked for relevant studies. Two independent reviewers performed study selection, critical appraisal, and data extraction. Results Although numerous HNAs exist, there is very limited research evidence of widespread implementation in clinical practice. Of the 660 studies identified, 113 abstracts were screened, and 5 studies were included in this review, yielding mixed results. Four studies were based in the UK and one in Canada. Only one study reported a brain-tumour-specific HNA tool for use in the neuro-oncology outpatient clinic. Studies consisted of heterogeneous study designs and small sample sizes. Conclusion HNAs are important for good patient experience as the assessment is used to address unmet patients’ needs and identify areas where extra support or signposting to other services is required. The studies reported mixed results on implementation of HNAs in clinical practice identifying important implications for patients with brain tumours. Impact The findings show the necessity for the development of a brain tumour specific HNA to address the specific needs of those with brain tumours at an individual patient and caregiver level.
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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 | 0.116 | 0.461 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".