Development of the Nova Scotia Potential Donor Audit (PDA) Tool and 2020 Historic Performance Database: Lessons Learned From the First 1000 Medical Record Reviews
Bibliographic record
Abstract
Background: Legislation and accountability frameworks are key components of high-performing deceased-donation systems. In 2021, Nova Scotia (NS), Canada, became the first jurisdiction in North America to enact deemed consent legislation and concurrently implemented mandatory referral legislation similar to that found in other Canadian provinces. Frontline financial resources were provided by the government to support the development of program infrastructure, including implementation of means to evaluate system performance. Methods: The Organ Donation Program (ODP), in collaboration with other stakeholders, developed a Potential Donor Audit (PDA) tool and database for referral intake and manual performance audits. Medical record reviews of deaths in the year before legislative change were conducted to pilot and revise the PDA and evaluate missed donation opportunities. Results: The NS PDA was piloted on 1028 patient deaths. Of 518 patients (50.4%) who met clinical triggers for referral to the ODP, 72 (13.9%) were referred (86.1% missed referral rate). One hundred sixty-three patients met the NS definition of a potential donor; 53 (32.5%) were referred (110 missed potential donors). Referral consent rates reached 71.7% (n = 38 of 53 approaches). The actualized donation rate reported by Canadian Blood Services was 29.9 donors per million population (n = 34 donors). Discussion: We documented high rates of missed referrals and missed potential donors before the enactment of mandatory referral and deemed consent legislation. Conclusions: The ODP has intentionally broadened clinical criteria for referral to shift the responsibility of identifying medically suitable potential donors from bedside clinicians to organ donation specialists. Lessons learned from our experience developing a PDA include the importance of early involvement of multiple stakeholders and ongoing modification of fields and workflow based on data availability and utility for clinical, educational, research, and reporting purposes.
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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 | 0.084 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".