Utilisation and outcomes of allogeneic hematopoietic cell transplantation in Ontario, Canada, and New York State, USA: a population-based retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: Allogeneic haematopoietic cell transplantation (HCT) is a potentially curative treatment for haematologic and oncologic diseases. There is a perception that the United States of America (USA) offers greater access to expensive therapies such as HCT. Alternatively, Canada is thought to suffer from protracted wait times, but lower spending. Our objective was to compare HCT utilisation and short-term outcomes in Ontario (ON), Canada, and New York State (NY), USA. DESIGN, SETTING AND PARTICIPANTS: We conducted a population-based cohort study using administrative health data to identify all residents of ON and NY who underwent allogeneic HCT between 2012 and 2015. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome measures were age and sex standardised HCT utilisation rates, in-hospital mortality, hospital length of stay (LOS) and readmission rates in ON and NY. Secondary outcomes included comparing ON and NY HCT recipients with respect to demographic characteristics and patient wealth (using neighbourhood income quintile). RESULTS: We identified 547 HCT procedures in ON and 1361 HCT procedures performed in NY. HCT recipients in ON were younger than NY (mean age 49.0 vs 51.6 years; p<0.001) and a lower percentage of ON recipients resided in affluent neighbourhoods compared with NY (47.2% vs 52.6%; p=0.026). Utilisation of HCT was 14.4 per 1 million population per year in ON and 26.7 per 1 million per year in NY (p<0.001). The magnitude of the ON-NY difference in utilisation was larger for older patients. In-hospital mortality, LOS and readmission rates were lower in ON than NY in both unadjusted and adjusted analyses. CONCLUSIONS: We found significantly lower utilisation of HCT in ON compared with NY, particularly among older patients. Higher in-hospital mortality in NY relative to ON requires further study. These differences are thought provoking for patients, healthcare providers and policy-makers in both jurisdictions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".