Patient-level costs of treating metastatic gastric cancer by treatment strategy.
Bibliographic record
Abstract
167 Background: Half of gastric cancer patients in North America present with metastatic disease. The appropriate management of these patients is complex and few guidelines exist to definitively guide treatment; as a result, practice variation exists. This variation may lead to care inefficiencies with adverse outcomes. We therefore aimed to conduct a patient-level exploration of metastatic gastric cancer management costs, by treatment strategy, and identify predictors of cost. Methods: We performed a patient-level cost analysis of metastatic gastric cancer patients diagnosed between 2005 and 2008 using a 26-month time horizon and the healthcare system’s perspective. Clinical data was derived from a provincial chart review. Costs associated with supportive care, radiation only, chemotherapy only, chemoradiation, gastrectomy, and gastrectomy with chemotherapy +/- radiotherapy were derived using administrative data from a universal healthcare system. Costs were inflated to 2017 United States dollars (USD) Linear regression was used to identify factors predictive of cost. Results: The absolute mean costs of metastatic gastric cancer management increased with increasing level of intervention and ranged from $34,002 to $72,778 (USD); supportive care was associated with the lowest costs and gastrectomy with chemotherapy +/- radiotherapy was associated with the highest costs. Age over 70 and lower Charlson Comorbidity Index were predictors of lower costs while supportive care, radiotherapy, chemoradiation, and gastrectomy were associated with higher costs. Conclusions: Practice variation has known clinical implications, but economic impact also needs to be considered. Variation in the management of metastatic gastric cancer may reflect dissimilar resource availability between health regions as well as differential access to palliative care. Evidence-based guidelines directing appropriate care for metastatic gastric cancer patients to reduce inefficiencies in care and governmental intervention to implement equitable resource allocation to bridge gaps in care are necessary.
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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 | 0.009 |
| 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 | 0.001 |
| 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".