Common patient‐reported sources of cancer‐related distress in adults with cancer: A systematic review
Bibliographic record
Abstract
BACKGROUND: Cancer-related distress (CRD) is widely experienced by people with cancer and is associated with poor outcomes. CRD screening is a recommended practice; however, CRD remains under-treated due to limited resources targeting unique sources (problems) contributing to CRD. Understanding which sources of CRD are most commonly reported will allow allocation of resources including equipping healthcare providers for intervention. METHODS: We conducted a systematic review to describe the frequency of patient-reported sources of CRD and to identify relationships with CRD severity, demographics, and clinical characteristics. We included empirical studies that screened adults with cancer using the NCCN or similar problem list. Most and least common sources of CRD were identified using weighted proportions computed across studies. Relationships between sources of CRD and CRD severity, demographics, and clinical characteristics were summarized narratively. RESULTS: Forty-eight studies were included. The most frequent sources of CRD were worry (55%), fatigue (54%), fears (45%), sadness (44%), pain (41%), and sleep disturbance (40%). Having enough food (0%), substance abuse (3%), childbearing ability (5%), fevers (5%), and spiritual concerns (5%) were infrequently reported. Sources of CRD were related to CRD severity, sex, age, race, marital status, income, education, rurality, treatment type, cancer grade, performance status, and timing of screening. CONCLUSIONS: Sources of CRD were most frequently emotional and physical, and resources should be targeted to these sources. Relationships between sources of CRD and demographic and clinical variables may suggest profiles of patient subgroups that share similar sources of CRD. Further investigation is necessary to direct intervention development and testing.
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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 | 0.009 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".