EPID-30. THE COST OF HOSPITAL-BASED CARE AMONG CANADIAN PATIENTS WITH MALIGNANT CENTRAL NERVOUS SYSTEM TUMOURS IN COMPARISON TO THOSE WITH LUNG AND COLORECTAL CANCERS IN THE YEAR PRECEDING THEIR CANCER DIAGNOSIS
Bibliographic record
Abstract
Abstract BACKGROUND Approximately 12% of the Canadian GDP was spent on healthcare in 2023. While hospital-based care (HC) constitutes a significant portion of this expenditure, the costs may vary depending on the cancer type. We investigate the HC cost of patients with malignant central nervous system tumours (CNS cancer) and compare it to patients with common cancers in Canadian provinces. METHODS We obtained data between 2010 and 2014 from the Canadian Institute for Health Information’s in-patient and ambulatory care datasets. HC cost was estimated using a 12-month look back period prior to diagnosis date for each patient, including cost related to cancer diagnosis and general healthcare. Choosing colorectal (CRC) and lung (LC) cancers to be the common cancers, patients who were 15 years of age or older and had a diagnosis of one of these cancers were included. We used quantile regression to investigate the factors associated with HC cost. RESULTS A total of 4,498 (CNS), 41,028 (CRC) and 29,747 (LC) patients were included from Alberta and Ontario, Canada. Encounter type (in-patient/out-patient), province (AB/ON), and comorbidities (Yes/No) were the top three factors contributing to HC cost. Adjusting for encounter type, comorbidities and age, the HC costs for CNS patients were $0.7k and $2.4k higher than CRC patients at 50th and 90th percentile, respectively, and $1.0k higher than LC patients at 90th percentile in Ontario. While in Alberta, HC costs for CNS patients were $0.2k and $0.8k higher than CRC patients at 50th and 90th percentile, respectively, and $1.0k lower than LC patients at both 50th and 90th percentiles. CONCLUSIONS We compared HC cost among Canadian patients with different cancer types; significant HC cost differences between patients with CNS and common cancer were observed. Further investigations are warranted to understand drivers of the cost differences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".