Deceased Organ Donation and the Nicholas Effect
Bibliographic record
Abstract
Nicholas Green was a 7-year-old American boy who was shot in the head during an attempted carjacking while vacationing in Italy on September 29, 1994 (date of neurological death October 1, 1994). In a heroic decision, his family then consented to donate five of his organs and both corneas to other patients awaiting transplantation. This altruistic choice made front-page news, was lauded by the President of Italy, blessed by the Pope, and contributed to a threefold increase in subsequent donations nationally (1). The case exemplifies how deceased organ donation can lead to a positive cascade of secondary behaviors among others in the community, a phenomenon now called the Nicholas Effect (2). A community health survey was developed containing a brief scenario to validate the Nicholas Effect in a randomized control design (Textbox 1). The survey was formulated in two versions (defined as the standard version or modified version) that differed in only one background sentence. The standard version described a situation with no mention of deceased organ donation. The modified version contained the same sentence expanded to describe the actuality of deceased organ donation. The two versions otherwise contained identical data presented in the same format to elicit a respondent’s willingness to reschedule a medical appointment for another patient’s convenience. Adults waiting in line at a popular coffee shop were surveyed (n=468). Each participant received one version of the survey (standard or modified) by individualized randomized blinded assignment. Overall, 82 of the 238 respondents in the standard version were willing to reschedule their appointment. In contrast, 113 of the 230 respondents in the modified version were willing to reschedule their appointment. Chi-square statistical testing indicated a 15% absolute increase in willingness to reschedule caused by the Nicholas Effect (34% vs. 49%, P=0.002), mathematically equal to a number-needed-to-treat of about seven.TEXTBOXES: SCENARIO WITH EXACT WORDING OF DIFFERENT VERSIONSBy design, this effect is more likely explained as empathy toward families willing to donate rather than as a negative attitude toward families unwilling to donate. These findings are difficult to attribute to response bias, selective sampling, failures of randomization, thoughtless respondents, or other survey artifacts (3, 4). Of course, the results have limitations because a brief hypothetical scenario was developed, community members were surveyed in a relaxed convenient setting, and did not explore the full range of altruistic behaviors (5). Despite these limitations, the results demonstrate the Nicholas Effect in a scientific manner and corroborate past anecdotes in the media (6, 7). An awareness of the Nicholas Effect might be potentially helpful in addressing ongoing shortfalls in deceased organ donation (8). At present, many families are uncertain and vacillate when facing a consent decision (9). Public service announcements might wish to mention the Nicholas Effect for community education and media campaigns that promote organ donation (10). Conversely, a lack of awareness of the Nicholas Effect could imply that some families may not make a fully informed decision when they choose to decline organ donation (11). Nicholas Green was an inspiration who made an enduring contribution to the lives of hundreds of Italians as well as to our understanding of the psychology of human altruism. Donald A. Redelmeier 1,2,3,4,5 Jason D. Woodfine1,2,3 1 Department of Medicine University of Toronto Toronto, Canada 2 Evaluative Clinical Sciences Program Sunnybrook Research Institute Toronto, Canada 3 Institute for Clinical Evaluative Sciences Sunnybrook Research Institute Toronto, Canada 4 Division of General Internal Medicine Sunnybrook Health Sciences Centre Toronto, Canada 5 Center for Leading Injury Prevention Practice Education & Research Sunnybrook Health Sciences Centre Toronto, Canada ACKNOWLEDGMENTS The authors thank the following individuals for helpful comments: Craig DuHamel, David Juurlink, Christopher Kandel, Sharon May, Damon Scales, Stephen Stich, Christopher Yarnell, and Michael Young.
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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 | 0.019 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".