Characterizing Distress, the 6th Vital Sign, in an Oncology Pain Clinic
Bibliographic record
Abstract
CONTEXT: The delineation of populations of cancer patients with complex symptoms can inform the planning and delivery of supportive care services. OBJECTIVES: We explored the physical, psychosocial, and practical concerns experienced by patients attending an ambulatory oncology symptom control clinic. METHODS: Patients attending a Pain Clinic at a large tertiary cancer centre were invited to complete screening measures assessing distress, pain, fatigue, anxiety, depression, and practical and psychosocial problems. A matched sample of patients who did not attend the Pain Clinic were selected as a comparison group. RESULTS: Of all eligible Pain Clinic patients, 46 (77%) completed the measures; so did 46 comparison group patients. The percentages of patients reporting distress (78.3%), pain (93.5%), and fatigue (93.5%) were higher among Pain Clinic patients than among the comparison patients. A higher percentage of Pain Clinic patients also reported multiple, severe, concurrent symptoms: 87% scored 7 or higher in at least one of the pain, fatigue, or distress scales, and 30.4% of the patients scored 7 or higher on all three. The most common problem areas were feeling a burden to others, trouble talking with friends and family, spirituality, and sleep difficulties. CONCLUSIONS: Higher levels of multiple, concurrent symptoms and psychosocial problems were found in Pain Clinic patients than in a group of patients who did not attend the Pain Clinic. Routine screening and triaging of cancer patients using a comprehensive and standardized panel of questions can facilitate symptom assessment and management, and can inform program planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".