Impact of Donation Physicians on Deceased Organ Donation: A Systematic Review
Bibliographic record
Abstract
Background: An emerging strategy to increase deceased organ donation is to use dedicated donation physicians to champion organ donation. We sought to conduct a systematic review of the effectiveness of donation physicians in improving organ donation outcomes. Objective: A systematic review was conducted following Cochrane principles. MEDLINE, Embase, and CINHAL databases were searched from inception to March 26, 2020. Methods: Quantitative studies examining the effects of donation physicians on all deceased organ donation outcomes were considered for inclusion. Review articles, editorials and opinion articles, and case studies were excluded. Study selection was completed independently by 2 team members; all discrepancies were resolved by consensus. Two team members independently extracted data from studies. Results: A total of 1017 studies were screened, and 12 met inclusion criteria. Included studies were published between 1994 and 2019. Half used an interrupted time series design (n = 6; 50%), 3 (25%) were cohort studies, and 3 (25%) used a before-and-after study design. Outcomes (reported in greater than 50% of included articles) included consent/refusal rate (n = 8; 67%), number of potential donors (n = 7; 58%), and number of actual donors (n = 7; 58%). Across studies and design types, there was an increase in potential organ donors ranging from 8% to 143% (Mdn = 33%), an increase in actual organ donors from 15% to 113% (Mdn = 27%), an increase in donor consent rate from -3% to 258% (Mdn = 12%), and an increase in deceased donor transplants from 13% to 24% (Mdn = 19%) following the introduction of donation physicians. Conclusions: Donation physicians have the potential to significantly improve deceased organ donation. Further implementation and evaluation of donation physician programs is warranted. However, implementation should be undertaken with a clear plan for a methodologically rigorous evaluation of outcomes.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".