Cost Trajectories for Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Health care spending is known to be highly skewed, with a small subset of the population consuming a disproportionate amount of health care resources. Patients with cancer are high-cost users because of high incremental health care costs for treatment and the growing prevalence of cancer. The objectives of the present study included characterizing cancer-patient trajectories by cost, and identifying the patient and health system characteristics associated with high health system costs after cancer treatment. METHODS: This retrospective cohort study identified Ontario adults newly diagnosed with cancer between 1 April 2009 and 30 September 2010. Costs of health care use before, during, and after cancer episodes were used to develop trajectories of care. Descriptive analyses examined differences between the trajectories in terms of clinical and health system characteristics, and a logistic regression approach identified predictors of being a high-cost user after a cancer episode. RESULTS: Ten trajectories were developed based on whether patients were high- or low-cost users before and after their cancer episode. The most common trajectory represented patients who were low-cost in the year before cancer, survived treatment, and continued to be low-cost in the year after cancer (31.4%); stage ii cancer of the male genital system was the most common diagnosis within that trajectory. Regression analyses identified increases in age and in multimorbidity and low continuity of care as the strongest predictors of high-cost status after cancer. CONCLUSIONS: Findings highlight an opportunity to proactively identify patients who might transition to high-cost status after cancer treatment and to remediate that transition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".