Explaining Differences Between Hospitals in Number of Organ Donors
Bibliographic record
Abstract
The shortage of donor organs calls for a careful examination of all improvement options. In this study, 80 Dutch hospitals were compared. They provided 868 donors in a 5-year period, constituting 91% of all donors in that period in The Netherlands. Multilevel regression analysis was used to explain the differences between hospitals. Potential explanatory variables were hospital-specific mortality statistics, donor policy and structural hospital characteristics. Of all donors, 81% came from one quarter of the hospitals, mainly larger hospitals. A strong relationship was found between the number of donors and hospital-specific mortality statistics. Hospitals with a neurosurgery department had additional donors. Seven hospitals systematically underperformed over a period of 5 years. If these hospitals were to increase their donor efficiency to their expected value, it would lead to an increase of 10% in the number of donors. Most donors are found in large hospitals, implying that resources to improve donor-recruitment should be channelled to larger hospitals. This study presents an efficient strategy toward a benchmark for hospitals of their organ donation rates. Some larger hospitals performed less well than others. This suggests that there is still room for improvement. There is no evidence for large undiscovered and unused pools of donor organs.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".