Risk Factors for Continuous Distress Over a 12-Month Period in Newly Diagnosed Cancer Outpatients
Bibliographic record
Abstract
This analysis examined demographic and medical factors associated with continuous distress in the year following cancer diagnosis. Patients completed the Distress Thermometer, Fatigue and Pain Thermometers, and anxiety and depression measures, at baseline, 3-, 6-, and 12 months. A total of 480 patients were grouped into three trajectories for distress, pain, fatigue, anxiety, and depression. Logistic regression analyses were conducted to determine risk factors associated with each symptom pattern. Females were more likely to report continuous distress. Predictors of the remaining outcomes included younger age; a diagnosis of head and neck, gastrointestinal, or prostate cancer; and receipt of chemotherapy and radiation therapy. By identifying risk factors for continuous distress, interventions can be implemented more efficiently and targeted to those who are at an elevated risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".