Hospital Demand for Blood Components in Ontario Correlates with Selected Diagnostic and Procedural Activities
Bibliographic record
Abstract
Abstract Abstract 4272 Introduction: Demand for blood components in Canada is a significant and growing contributor to health care spending, but the drivers of this demand remain poorly characterized. While most institutions do not routinely monitor the transfusion requirements of individual diagnoses and procedures, an institution's overall demand for blood components and the types of clinical activities it provides are both well documented. Development of an institution-level inferential model may allow for more accurate demand forecasting for blood components. Methods: Hospital shipments of all blood components from Canadian Blood Services (red blood cells, platelets, plasma and cryoprecipitate) in the province of Ontario during the period 2006–2009 were merged with institution-level administrative data containing hospital characteristics and selected diagnostic and procedural codes as obtained from inpatient and ambulatory clinical databases maintained by the Canadian Institute for Health Information. Simple linear correlation and several nested multivariable linear regression models were fitted and compared. Results: From 2006–2009, our sample included 137 healthcare facilities, representing approximately 1.4 million units of RBCs, 400 000 units of plasma, 166 000 doses of platelets (defined as either one pool of whole blood-derived platelets or a single apheresis platelet), and 20 000 units of unpooled cryoprecipitate. Institutional demand for these blood components correlated with both hospital-level characteristics and diagnostic and procedural activity data. Institutional demand for red blood cells correlated most strongly with visits for cirrhosis and chemotherapy. Demand for plasma correlated most strongly with visits for cirrhosis and cardiac surgery procedures. Demand for platelets correlated most strongly with bone marrow and solid organ transplant procedures. Demand for cryoprecipitate correlated most strongly with visits for valvular heart disease and cardiac procedures. The strength of these correlations generally remained stable over the four years of analysis, during which time demand for red blood cells and platelets increased, demand for plasma decreased, and demand for cryoprecipitate remained stable. Conclusions: In the largest province of Canada, institutional demand for blood components correlate strongly with disease burden measured by diagnostic and procedural codes contained within administrative data. Disclosures: Pendergrast: Canadian Blood Services: Research Funding. Zagorski:Canadian Blood Services: Research Funding.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".