Patient-Reported Symptom Burden as a Predictor of Emergency Department Use and Unplanned Hospitalization in Head and Neck Cancer: A Longitudinal Population-Based Study
Bibliographic record
Abstract
PURPOSE: To determine the association between patient-reported symptom burden and subsequent emergency department use and unplanned hospitalization (ED/Hosp) in a head and neck cancer (HNC) patient population. METHODS: This was a population-based study of patients diagnosed with HNC who had completed at least one outpatient Edmonton Symptom Assessment System (ESAS) assessment between January 2007 and March 2018 in Ontario, Canada. Logistic regression models were used to determine the relationship between outpatient ESAS scores and subsequent 14-day ED/Hosp use. A generalized estimating equation approach with an exchangeable correlation structure was incorporated to account for patient-level clustering. RESULTS: There were 11,761 patients identified, completing a total of 73,282 ESAS assessments and experiencing 5,203 ED/Hosp events. Six of the nine ESAS symptom scores were positively associated with ED/Hosp use, with pain, appetite, shortness of breath, and tiredness demonstrating the strongest associations. A global ESAS score was calculated by selecting the highest individual symptom score (h-ESAS). Among patients reporting a maximum h-ESAS score of 10, 15.1% had an ED/Hosp event within 14 days compared with 1.5% for those with the lowest possible score of zero. In adjusted analysis, the odds of ED/Hosp use increased with h-ESAS (1.23 per one-unit increase [95% CI, 1.22 to 1.25]). When treated as a categorical variable, patients with the maximum h-ESAS score of 10 had 9.23 (95% CI, 7.22 to 11.33) higher odds of ED/Hosp use, relative to the minimum score of zero. CONCLUSION: ESAS scores are strongly associated with subsequent ED/Hosp events in patients with HNC. Clinician education around how ESAS data might inform patient care may enhance symptom detection and management.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".