High levels of untreated distress and fatigue in cancer patients
Bibliographic record
Abstract
The purpose of the study was to assess a large representative sample of cancer patients on distress levels, common psychosocial problems, and awareness and use of psychosocial support services. A total of 3095 patients were assessed over a 4-week period with the Brief Symptom Inventory-18 (BSI-18), a common problems checklist, and on awareness and use of psychosocial resources. Full data was available on 2776 patients. On average, patients were 60 years old, Caucasian (78.3%), and middle class. Approximately, half were attending for follow-up care. Types of cancer varied, with the largest groups being breast (23.5%), prostate (16.9%), colorectal (7.5%), and lung (5.8%) cancer patients. Overall, 37.8% of all patients met criteria for general distress in the clinical range. A higher proportion of men met case criteria for somatisation, and more women for depression. There were no gender differences in anxiety or overall distress severity. Minority patients were more likely to be distressed, as were those with lower income, cancers other than prostate, and those currently on active treatment. Lung, pancreatic, head and neck, Hodgkin's disease, and brain cancer patients were the most distressed. Almost half of all patients who met distress criteria had not sought professional psychosocial support nor did they intend to in the future. In conclusion, distress is very common in cancer patients across diagnoses and across the disease trajectory. Many patients who report high levels of distress are not taking advantage of available supportive resources. Barriers to such use, and factors predicting distress and use of psychosocial care, require further exploration.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".