Bibliographic record
Abstract
Abstract Background Multiple myeloma (MM) accounts for about 10% of hematologic malignancies, and the 5-year survival rate of patients diagnosed with multiple myeloma has increased by 14% over the past two decades with median survivals between 29-62 months. Improvements in survival has been attributable to improved therapeutics, representing a significant expenditure within the healthcare system. We seek to quantify the cost for caring for patients with MM from a third-party perspective. Methods We conducted a descriptive retrospective cohort costing study using administrative data from the Cancer Measurement Outcomes Research and Evaluations (C-MORE) at the Tom Baker Cancer Centre (TBCC). We identified incident cases of patients diagnosed with symptomatic multiple myeloma between 01 Jan 2002 and 31 Dec 2014. Demographic data were obtained together with their contact points with the healthcare system, including inpatient visits and lengths of stay, outpatient visits diagnostic and treatment visits, as well as medication usage. Using the Alberta Health Hospital Reciprocal Claim Guide and local pharmacy costs at TBCC, we assigned costs to health care utilization for incident cases of MM between our study period. Further, we stratified our data into 3 cohorts: 2002-2005, 2006-2010 and 2011-2014 to better appreciate secular trends. Results We identified 806 patients, but excluded 284 patients (35%) due to absence of data. The median duration of follow-up of the remaining cohort was 46 months (range 1-172). The median number of lines of therapy is 2 (range 1-8) with 32% overall enrolled in at least one clinical study. Table 1 details the cohort demographics with an overall survival (OS) of cohort of 55% (median survival of 81 months, 95% CI=68-94). The average cost of care per patient per year is $49,077, with $30,895, $46,333, $69,627 for Cohorts 1, 2 and 3 respectively. Chemotherapy (not including clinical trial medications) costs represents the largest cost-item. Conclusions Our study suggests that the survival of patients with MM diagnosed at the TBCC has improved consistent with the literature, but with an increasing cost of care. Despite incomplete administrative data from C-MORE, the current available data could help support health care Administrators better prioritize health care resources. Table 1. Table 1. Disclosures No relevant conflicts of interest to declare.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".