Symptom burden as a predictor of emergency room use and unplanned hospitalization in patients with head and neck cancer: A population-based study.
Bibliographic record
Abstract
12084 Background: Symptoms are common in oncology patients, though they remain undetected and untreated by clinicians in up to 50% of cases. Integrating patient reported outcomes (PRO) within routine clinical practice has been suggested as a way to improve detection. In order to inform an effective and efficient PRO symptom screening program, we sought to determine whether outpatient symptom scores could predict emergency room use and unplanned hospitalization (ER/Hosp) in a cancer patient population. Methods: This was a population-based study of patients diagnosed with head and neck cancer who had completed at least one outpatient Edmonton Symptom Assessment System (ESAS) assessment between January 2007 and March 2018 in Ontario. Logistic regression models were used to determine the relationship between reported outpatient ESAS scores and ER/Hosp use in the 14-day period following ESAS completion. A generalized estimating equations approach was incorporated to account for possible patient-level clustering. Results: There were 11,761 unique patients identified with a total of 73,282 ESAS assessments. There were 5,203 ER/Hosp outcome events. In adjusted analysis, the odds of ER/Hosp use increased log linearly with ESAS score (1.23 per 1 unit increase in index ESAS score, [95% confidence interval (CI) 1.22 – 1.25]). This corresponds to a 9.23 (95%CI 7.22-11.33) higher odds of ER/Hosp use for the maximum index ESAS score of 10. Seven of the nine ESAS symptom scores were significantly associated with ER/Hosp use with pain, appetite and shortness of breath demonstrating the strongest association. Conclusions: ESAS scores are independently associated with 14-day ER/Hosp in head and neck cancer patients. Appropriate and timely management of symptom burden may reduce rates of ER/Hosp. [Table: see text]
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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 | 0.002 |
| 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".