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 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 | 0.034 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".