Implementing Routine Screening for Distress, the Sixth Vital Sign, for Patients With Head and Neck and Neurologic Cancers
Bibliographic record
Abstract
This study examined the benefits of incorporating screening for distress as a routine part of care for patients with head and neck and neurologic cancers in a tertiary cancer center. Using a comparative 2-cohort pre-post implementation sequential design, consecutive outpatients with head and neck and neurologic cancers were recruited into 2 separate cohorts. Cohort 1 included patients attending clinics during April 2010, before the implementation of the screening program. The program was then implemented and patients completed the Screening for Distress Minimum Dataset (the Edmonton Symptom Assessment System [ESAS] and the Canadian Problem Checklist [CPC]) at each clinic visit. Cohort 2 included patients attending clinics during March 2011. Consenting patients completed screening and outcome measures (ESAS, CPC, and either the Functional Assessment of Cancer Therapy-Brain or the Functional Assessment of Cancer Therapy-Head and Neck). A total of 146 patients (78 head and neck and 68 neurologic) provided data for Cohort 1, and 143 (81 head and neck and 62 neurologic) provided data for Cohort 2. Compared with Cohort 1, patients with neurologic cancers in Cohort 2 reported significantly higher scores on the Functional Assessment of Cancer Therapy: General total and emotional quality of life subscale; fewer high scores (≥ 4) on the ESAS breathlessness item; and fewer problems with fears/worries, frustration/anger, finding meaning in life, and worry about friends/family. Head and neck patients in Cohort 2 reported significantly higher emotional quality of life and fewer problems with eating and weight than those in Cohort 1. Although no definitive causal attributions can be made, patients exposed to routine screening for distress reported better well-being and fewer emotional, physical, and practical problems than historical controls.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".