What to do with screening for distress scores? Integrating descriptive data into clinical practice
Bibliographic record
Abstract
OBJECTIVE: Implementation of routine Screening for Distress constitutes a major change in cancer care, with the aim of achieving person-centered care. METHOD: Using a cross-sectional descriptive design within a University Tertiary Care Hospital setting, 911 patients from all cancer sites were screened at the time of their first meeting with a nurse navigator who administered a paper questionnaire that included: the Distress Thermometer (DT), the Canadian Problem Checklist (CPC), and the Edmonton Symptom Assessment System (ESAS). RESULTS: Results showed a mean score of 3.9 on the DT. Fears/worries, coping with the disease, and sleep were the most common problems reported on the CPC. Tiredness was the most prevalent symptom on the ESAS. A final regression model that included anxiety, the total number of problems on the CPC, well-being, and tiredness accounted for almost 50% of the variance of distress. A cutoff score of 5 on the DT together with a cutoff of 5 on the ESAS items represents the best combination of specificity and sensitivity to orient patients on the basis of their reported distress. SIGNIFICANCE OF RESULTS: These descriptive data will provide valuable feedback to answer practical questions for the purpose of effectively implementing and managing routine screening in cancer care.
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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 | 0.004 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".