Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Brief Kidney Disease: Improving Global Outcomes (KDIGO) engaged an evidence review team and convened a work group to produce a guideline to evaluate and manage candidates for living kidney donation. The evidence for most guideline recommendations is sparse and many “ungraded” expert consensus recommendations were made to guide the donor candidate evaluation and care before, during, and after donation. The guideline advocates for replacing decisions based on assessments of single risk factors in isolation with a comprehensive approach to risk assessment using the best available evidence. The approach to simultaneous consideration of each candidate’s profile of demographic and health characteristics advances a new framework for assessing donor candidate risk and for defensible shared decision making. One of the most important tasks we have to undertake is to evaluate and then care for living donors. This paper provides a summary of the KDIGO Guidelines and encapsulates what you will find in the guidelines themselves, which are published in full in a separate supplement. Care of our living kidney donors is not an evidence free void for personal opinion and practice to fill–there are data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it