Funding model for stem cell transplant.
Bibliographic record
Abstract
22 Background: A call for development of a new model for funding Stem Cell Transplant (SCT) services in Ontario led to a costing initiative described below. The new model needed to be in alignment with current costs and clinical practices, and to avoid over or under-funding the required components of care for all types of transplant (autologous, allogeneic-related, allogeneic un-related). Methods: The patient care path was broken down by transplant type into the following phases: Pre-Transplant, Graft Harvesting or Procurement, Transplant, and Follow-Up. The following Canadian and Ontario data sources were used for fiscal year 2013/14 and 2014/15 to identify costs of care: -Case Costing data submitted by the six hospitals providing SCT services, -Discharge Abstract Database (DAD), -National Ambulatory Care Reporting System (NACRS), -Other data sources, as needed (e.g. Specialized Services Oversight Information System, Health Indicator Tool). Additionally, the costs for high cost drugs, infrastructure, psychosocial support, and pathology services components were specifically identified. Broad consultation with clinical, administrative, and costing experts ensured the data was complete and appropriate to patient care needs. Once the costs for each component and phase were estimated, the funding model was developed using the bundling approach where the costs of specific activities and/or phases were bundled together for the purposes of funding. Results: The SCT Funding Model developed based on this costing initiative consists of the following four bundles covering the continuum of a patient’s care from pre-transplant to post-transplant care follow-up: 1.) Pre-Transplant, 2.)Graft Acquisition, 3.)Transplant (includes transplant and follow-up), and 4.) High Cost Drugs. Conclusions: The new SCT Funding Model reflects up-to-date costs and addresses the gaps of the previous approach. It has been implemented and is widely supported due to the extensive stakeholder engagement throughout the process. It is an important component of provincial-level planning for SCT service delivery.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".