A systematic approach for high-quality care in complex malignant hematology.
Bibliographic record
Abstract
57 Background: Capacity limits in Ontario, Canada, resulted in long waits and out-of-country care for hematopoietic cell therapy (HCT) and inconsistent access for acute leukemia (AL) patients. A multi-pronged system improvement approach was implemented to ensure high quality care as close to home as possible. Methods: Robust forecasting models and physician workload benchmarks were developed to quantify needs and drive health human resource and capital planning. Clinical and organizational guidelines were developed for new models of care, pathology and laboratory medicine services, and networks of care. Funding models were introduced to support care needs and service models. A comprehensive measurement strategy, including patient reported experience measures, was developed. Results: Six services sites providing HCT and AL care are networked with four AL sites and three supporting sites. Capital expansion projects have been completed and others continue. Two biomarker reference centers were established to serve as quality leads and ensure timely testing. Average turnaround time is 12 days for cytogenetic testing. 19 additional physician specialists and 5 additional fellowships were approved for allocation across Ontario. Three nurse practitioners participated in mentorship programs. Access has improved and wait times are monitored. In 2018 there were 736 autologous and 357 allogeneic transplants done vs 396 and 159 respectively in 2014. 43 patients were referred out of country in 2016 compared with two in 2018, with a median wait of 70 days from AL remission to transplant in 2018. Nine (of 14) regional cancer centers offer outpatient AL consolidation. Patient experience was highest in treatment planning, physical comfort and patient preferences. Conclusions: A multi-pronged approach to planning, funding and quality assurance resulted in measureable increased capacity and high quality care closer to home.
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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 | 0.098 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".