Regional variation in the costs of treating curative gastric cancer.
Bibliographic record
Abstract
165 Background: Gastric cancer is a highly morbid and fatal disease. While the clinical challenge with gastric cancer is widely described, little is known about the economic burden of the disease. Many guidelines outline the curative treatment for gastric cancer and in a universal healthcare system, equal access and uniform healthcare delivery is predicted. Yet, regional variation in practice exists and may result in increased healthcare costs. We therefore aimed to investigate of the costs of treating curative gastric cancer, explore regional variation in costs, and to identify the factors which drive these costs. Methods: We conducted a patient-level cost analysis of curative-intent stage I-III gastric cancer diagnosed between 2005 and 2008 from the perspective of a universal healthcare system, using a 26-month time horizon. Clinical and stage data were abstracted from a provincial chart review. Costs associated with physician billings, same day surgery, hospitalization, drug benefits, emergency department visits, continuing care, and long-term care were derived using administrative healthcare databases and compared among healthcare regions. Costs were inflated to 2017 United States dollars (USD). We used a linear regression model to identify factors predictive of these treatment costs. Results: A total of 722 patients with Stage I-III gastric cancer were identified. Mean costs per region ranged from $55,650 to $92,852 (USD) across 14 health regions. The lowest contributing cost sector was long-term care and the highest contributing proportion of costs was from hospital admissions. A laparoscopic surgical approach was predictive of lower costs while age over 70 years and death at one year from diagnosis was predictive of higher costs. Conclusions: Regional variation exists in the treatment costs for patients with curative gastric cancer despite guidelines directing appropriate care and healthcare delivery in a universal healthcare system. Governmental intervention to ensure quality care delivery is necessary for improved and sustainable curative gastric cancer care across regions.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".