116.6: Deceased donation reform: a qualitative assessment of impact
Bibliographic record
Abstract
In April 2019, Nova Scotia, a province in Canada, passed updated legislation that included deemed consent. Nova Scotia is the first jurisdiction in all of North America to implement an opt-out model, deemed consent model, for deceased donation. Further to this, major system reforms occurred with the changes in legislation. Specific legislative reforms included mandatory referral for deceased donation, change of consent model to deemed consent, and enabling donor and recipient connection. Major system transformations included implementation of donor physicians at key regional and tertiary care centres, total revision of the potential donor audit tool and approach, development and implementation of a donor management system, and increased public and healthcare professional education. Using a qualitative assessment approach, the known impact to the provincial deceased donor system and the current impact from the legislative reforms will be clearly articulated. Key performance indicators, including referral rates, approach and consent rates, donor conversation rates, and consented and utilized donor rates will be discussed at length to demonstrate where key changes have impacted the provincial deceased donor system. To date, preliminary results have demonstrated the following: Increase in organ donation referrals by 130% Increase in tissue donation referrals by 228% Increase in actual tissue donors by 40% Although it is still early in the implementation, ongoing analysis is occurring. Nova Scotia has undertaken a complete revision to their deceased donation program. Rarely is there an ability to examine system and legislative changes over a period of time to determine where best practices have the greatest impact on deceased donation. This presentation will provide a 2-year summary of experiences and outcomes to date that will help to confirm which changes have had the largest impact to date.
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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 | 0.042 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".