Patient Reported Symptom Burden and its Association with Emergency Department Use and Unplanned Hospitalization: A Multi-methods Approach to Improving Head and Neck Cancer Care in Ontario
Bibliographic record
Abstract
The goal of this thesis is to enhance outpatient symptom management protocols for head and neck cancer patients. This goal is supported by three objectives: (1) to identify predictors of emergency department use and unplanned hospitalization in head and neck cancer; (2) to establish the association between patient-reported symptom burden and emergency department use/unplanned hospitalization; and (3) to explore how head and neck cancer patients cope with cancer-related symptoms at home, and to examine their perspective on standardized symptom assessment. Objective 1: Through a systematic review, consistent risk factors for emergency department use and unplanned hospitalization were identified. Risk factors related to either ‘patient-related’, ‘cancer severity’ or ‘process’ factors. Objective 2: Through a population-based study, a strong dose-response effect was observed between patient-reported symptom burden and acute care events. Six of the nine symptom scores were positively associated with emergency department use/unplanned hospitalization. Pain, appetite, shortness of breath, and tiredness demonstrated the strongest associations. Patients with high symptom scores had nearly 10 times higher odds of an acute care event relative to low symptom scores. Objective 3: Through patient interviews, physical, emotional, and informational care gaps were identified. Perceptions of Ontario’s standardized symptom assessment program varied. Some patients saw immense value, while others felt disempowered by the process, particularly in cases where high symptom scores were not reviewed or acted on. These data stress the importance of proactive symptom management. Collectively, these findings provide concrete recommendations for enhanced symptom monitoring in the head and neck cancer population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".