Do High Symptom Scores Trigger Clinical Actions? An Audit After Implementing Electronic Symptom Screening
Bibliographic record
Abstract
PURPOSE: Standardized, electronic, symptom assessment is purported to help identify symptom needs. However, little research examines clinical processes related to symptom management, such as whether patients with worsening symptoms receive clinical actions more often. This study examined whether patient visits with higher symptom scores are associated with higher rates of symptom documentation in the chart and symptom-specific actions being taken. METHODS: Retrospective chart reviews on cancer patient visits at a regional cancer center. An electronic Edmonton Symptom Assessment Scale (ESAS), a validated tool to measure symptoms, was implemented center-wide to standardize symptom screening at every patient visit. The independent variable was ESAS scores for pain and shortness of breath, categorized by severity: 0 (none), 1-3, 4-6, 7-10 (severe). Outcomes included symptom documentation in the chart on the visit date and symptom-related action(s) taken within 1 week. RESULTS: Nine hundred twelve visits were identified. Pain and shortness of breath were documented in 51.8% and 29.7% of charts, and a related-action occurred in 16.9% and 3.9% of charts, respectively. As ESAS severity score category increased from none to severe, the proportion of visits with pain documented increased significantly (36.9%, 49.2%, 55.2%, and 71.4%; P < .001). Likewise, as ESAS score severity increased, the proportion of visits with a pain-related action increased significantly (4.2%, 10.6%, 21.3%, and 37.0%; P < .001). Trends were similar for shortness of breath. CONCLUSION: Results show a positive association between higher symptom scores and higher rates of documentation and clinical actions taken. However, symptom-related actions were documented in a minority of visits in which symptoms were noted as severe.
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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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".