Non-Parametric Statistical Analysis of Rare Events in Healthcare
Bibliographic record
Abstract
The assumption of Gaussian distribution of population does not always hold strongly in health studies. The sample size may not be large enough due to the limited nature of observations such as biopsies taken during kidney transplantation, the distribution of sample may not be Gaussian, or the observation may not even be possible for the far ends of a Gaussian distribution. In such cases, an alternative approach, called nonparametric tests can be applied. In this study, a non-parametric single center retrospective analysis of adult kidney transplant is performed to compare histological outcomes among three different groups of deceased kidney donors, based on the biopsies taken before and after kidney transplant at months 1, 3, and 12. A total of 107 transplants were observed in this study with 310 surveillance biopsy taken then classified based on the Banff 97 adequacy assessment. It is concluded that the recipient's internal condition after kidney transplant is as important as the donor's risk factors.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.002 |
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".