American Society of Nephrology Quiz and Questionnaire 2015: ESRD/RRT
Bibliographic record
Abstract
The Nephrology Quiz and Questionnaire remains an extremely popular session for attendees of the Annual Kidney Week Meeting of the American Society of Nephrology. During the 2015 meeting, the conference hall was once again overflowing with eager quiz participants. Topics covered by the experts included electrolyte and acid-base disorders, glomerular disease, ESRD and dialysis, and kidney transplantation. Complex cases representing each of these categories together with single best answer questions were prepared and submitted by the panel of experts. Before the meeting, training program directors of nephrology fellowship programs and nephrology fellows in the United States answered the questions through an internet-based questionnaire. During the live session, members of the audience tested their knowledge and judgment on the same series of case-oriented questions in a quiz. The audience compared their answers in real time using a cellphone application containing the answers of the nephrology fellows and training program directors. The results of the online questionnaire were displayed, and then, the quiz answers were discussed. As always, the audience, lecturers, and moderators enjoyed this highly educational session. This article recapitulates the session and reproduces selected content of educational value for the readers of the Clinical Journal of the American Society of Nephrology Enjoy the clinical cases and expert discussions.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.014 |
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".