Health Care Utilization for Patients with Triple Class Exposed Relapsed and Refractory Multiple Myeloma: Experience from a Canadian Center
Bibliographic record
Abstract
Background: Multiple Myeloma (MM) is a plasma cell disorder characterized by the presence of remissions and relapses. The purpose of this project was to explore Canadian-specific real-world treatment patterns, and healthcare resource utilization (HCRU) in triple class exposed (TCE) relapsed/refractory MM (RRMM) patients in Canada. Methods: This is a retrospective observational cohort study using multiple administrative databases in Alberta, Canada. Outcomes were captured for TCE patients receiving a subsequent line of therapy (LOT) and included: treatment regimen details, time to next treatment (TTNT), overall survival (OS), and health care utilization. Results: Briefly, 567 TCE RRMM were identified. A median of 50 unique healthcare encounters were observed. In addition, 30.2% of readmissions were seen at 30 days from the start of next therapy in patients with TCE RRMM. On average at the patient level, there were 2 emergency department visits, 2 inpatient admissions, 51 total clinical visits and 5 infusion appointments. Overall, the average days spent on lab tests was 27 and the median duration of each inpatient admission was approximately 6-7 days. Median overall survival from initiating next line of therapy in TCE RRMM was 18.7 months (16-24.3 months). Conclusions: The present data provides further insights about healthcare resource utilization for patients with TCE RRMM in Alberta, Canada and may inform healthcare system planning as novel therapies continue to be developed for this patient population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".