Trends and predictors of unplanned hospitalization among oral and oropharyngeal cancer patients; an 8-year population-based study
Bibliographic record
Abstract
PURPOSE: The incidence of oral cancers, particularly HPV-related oropharyngeal cancer, is steadily increasing worldwide, presenting a significant healthcare challenge. This study investigates trends and predictors of unplanned hospitalizations for oral cavity cancer (OCC) and oropharyngeal cancer (OPC) patients in the province of Alberta, Canada. METHODS: This retrospective, population-based, cohort study used administrative data collected from all hospitals in the province. Using the Alberta Cancer Registry (ACR), a cohort of adult patients diagnosed with a single primary OCC or OPC between January 2010 and December 2017 was identified. Linking this cohort with the Discharge Abstract Database (DAD), trends in hospitalizations, primary diagnoses, and predictors of unplanned hospitalization (UH) and 30-day unplanned readmission were analyzed. RESULTS: Of 1,721 patients included, 1,244 experienced 2,228 hospitalizations, with 48 % being categorized as UH. The UHs were significantly associated with a higher mortality rate, 18.5 % as compared to 4.6 % for planned, and influenced by sex, age groups, comorbidities, cancer types, stages, and treatment modalities. The rate of UH per patient decreased from 0.69 to 0.54 visits during the study period (P = 0.02). Common diagnoses for UH were palliative care and post-surgical convalescence, while surgery-related complications such as infection and hemorrhage were frequent in 30-day unplanned readmissions. Predictors of UH included cancer stage, material deprivation, and treatment, while cancer type and comorbidity predicted readmissions. CONCLUSION: The rate of UHs showed a noteworthy decline in this study, which could be a result of enhanced care coordination. Furthermore, identified primary diagnosis and predictors associated with UHs and readmissions, provide valuable insights for enhancing the quality of care for cancer patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".