Symptoms and concerns amongst cancer outpatients: identifying the need for specialist palliative care
Bibliographic record
Abstract
This study aimed to define and prioritize the need for specialist palliative care (SPC) in cancer outpatient clinics. A validated assessment tool, the Symptoms and Concerns Checklist, was used to determine the prevalence and severity of symptoms and concerns. The checklist was completed by 480 outpatients with a cancer diagnosis. Sixty patients from each of eight primary tumour groups (lung, breast, gastrointestinal, gynaecological, urological, head and neck, brain and lymphoma) were recruited. The majority of patients (over 90%) rated 27 of the 29 checklist items, reporting a mean of 10 items as current problems. The influences of disease site and status, demographic factors and treatment on the number and type of symptoms and concerns reported were investigated. The highest number of symptoms and concerns and most severe problems were reported by patients with lung cancer, followed by those with brain tumours; the lowest by those with lymphoma and urological tumours. A high proportion of patients (83%) reported one or more items likely to benefit from SPC intervention. The results of this study suggest an extensive need for better symptom control in all cancer outpatients and in centres where SPC resources are limited, priority could be given to patients attending lung and brain tumour clinics.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".