P.470: Public awareness and interest in kidney donation: An analysis from January 2004 to March 2024.
Bibliographic record
Abstract
Background and objectives: Kidney donation provides a lifeline for patients with end-stage kidney disease. In the digital age, the Internet serves as a primary source for individuals seeking health-related information. However, public awareness and global interest in kidney donation post-COVID era remain unclear. This study aims to analyze global interest in kidney donation to gauge public awareness and interest over time. Design, setting, participants, and measurements: Utilizing Google Trends, we investigated the search terms “kidney donation” and “kidney donor” from January 2004 to March 2024. The analysis encompassed worldwide search trends and highlighted the top countries by relative search interest. The study compared peaks and troughs in search interest to identify patterns and potential trends in public interest. Results: The investigation period revealed fluctuating interest levels, with “kidney donation” peaking in March 2004 (score of 100) and reaching its lowest in October 2006 (score of 32). For “kidney donor,” the highest interest was also in March 2004 (score of 100), with the lowest point in April 2020 (score of 30). The most recent data from April 2024 showed a moderate resurgence in interest (scores of 58 and 55, respectively, for “kidney donation” and “kidney donor”). Canada and the United States emerged as leaders in search interest for “kidney donation,” whereas the United States and India were at the forefront for “kidney donor.” ARIMA forecasting indicated detectable fluctuations in global interest but no significant long-term changes. Conclusions: The study highlights a consistent global interest in kidney donation and donors, with significant variances over time but no dramatic shifts anticipated in the near future. The varied interest across countries underscores the global reach and importance of kidney donation. The findings signal the need for continued efforts to raise public awareness and educate on the importance of kidney donation to ensure widespread and equitable information dissemination.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".