Development of national system performance metrics for tissue donation, production, and distribution activity
Bibliographic record
Abstract
Canada's federal, provincial, and territorial governments gave Canadian Blood Services a mandate for organ and tissue donation and transplantation, including system performance, data and analytics. In 2012 Canadian Blood Services facilitated an eye and tissue banking workshop focused on standardized specifications and practices. At the workshop, the Canadian tissue community directed Canadian Blood Services to facilitate the development and implementation of a national data stream and analytics. Prior to this no national data was prospectively collected or collated on tissue donation, production or distribution activity. An eye and tissue data committee was formed with representation from eye and tissue banks in all Canadian jurisdictions. A minimum data set, standardized definitions, a data submission form and a quality assurance process was developed. Training was provided to data personal identified by each eye and tissue bank. Data collection was initiated January 1, 2013; with quarterly data submitted to Canadian Blood Services via excel spreadsheet. Data was submitted by sixteen Canadian eye and tissue banks, located in eight of Canada's thirteen provinces and territories, representing a census of activity. Annual data reports, with trend analysis, are generated and distributed to the tissue community to inform operational strategy and system performance improvement. This report provides an overview of the data process and provides visibility to the Canadian tissue donation, production and distribution activities for 3 years; January 1, 2013 to December 31, 2015.
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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 | 0.051 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".