Prevalence of psychosocial distress in cancer patients across 55 North American cancer centers
Bibliographic record
Abstract
Routine distress screening in United States oncology clinics has been mandatory since 2015. OBJECTIVE: This study was the first to assess distress in a geographically diverse sample of cancer patients following mandated distress screening implementation by oncology social workers. METHODS: Sites were self-selected via social workers who applied to participate in the Association of Oncology Social Work's Project to Assure Quality Cancer Care, advertised through their social media outlets and conference. Electronic screening records were collected from 55 cancer treatment centers in the United States and Canada. Cases required cancer diagnoses and Distress Thermometer (DT) scores to be included. Distress rates and rates by age, sex, cancer type, and ethnicity were examined. RESULTS: Of 4664 cases, 46% (2157) experienced significant distress (DT score ≥ 4). Being female, age 40-59, and having diagnoses of pancreatic or lung cancer was associated with increased likelihood of distress. Half of cases experience clinically-significant distress, though this need was not evenly distributed across patient or cancer types. CONCLUSION: Identifying those at risk for distress may help inform optimal resource allocation. Methods to address needs of distressed patients in cases of limited resources are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".