Treat patient, not just the disease: holistic needs assessment for haematological cancer patients
Bibliographic record
Abstract
Haematological malignancies can have devastating effects on the patients' physical, emotional, psycho-sexual, educational and economic health. With the improvement of therapies patients with these malignancies are living longer, however significant proportion these patient show poor quality of life (QoL) due to various physical and psychological consequences of the disease and the treatments. Health-related QoL (HRQoL) is multi-dimensional and temporal, relating to a state of functional, physical, psychological and social/family well-being. Compared with the general population, HRQoL of these patients is worse in most dimensions. However without routine holistic need assessment (HNA), clinicians are unlikely to identify patients with clinically significant distress. Surviving cancer is a chronic life-altering condition with several factors negatively affecting their QoL, such as psychological problems, including depression and excessive fear of recurrence, as well as social aspects, such as unemployment and social isolation. These need to be adequately understood and addressed in the healthcare of long-term survivors of haematological cancer. Applying a holistic approach to patient care has many benefits and yet, only around 25% of cancer survivors in the UK receive a holistic needs assessment. The efforts of the last decade have established the importance of ensuring access to psychosocial services for haematological cancer survivors. We need to determine the most effective practices and how best to deliver them across diverse settings. Distress, like haematological cancer, is not a single entity, and one treatment does not fit all. Psychosocialoncology needs to increase its research in comparative effectiveness.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".