My lifetime in <scp>IgA</scp> nephropathy: An <i>unexpected</i> journey
Bibliographic record
Abstract
UNEXPECTED ENCOUNTER WITH IgA NEPHROPATHYMy unexpected journey began when my wife, Rachel, and I in 1975 pursued our postdoctoral medical training at the University of Kentucky, hers in psychiatry and mine in internal medicine.I planned to be a general practitioner in a small town.In 1978, Rachel unexpectedly decided to extend her training to four years, one year more than my three-year residency.Instead of a one-year position as an internist in the emergency department of a local hospital, I started a two-year fellowship in nephrology during which time I encountered many patients with IgA nephropathy.I worked with Robert J. Wyatt, a pediatric nephrologist at the University of Kentucky, who investigated complement's role in kidney disease.In the middle of my fellowship training, four nephrology faculty unexpectedly moved to the University of Alabama at Birmingham (UAB).I remained in Lexington rather than relocating to Birmingham to complete my fellowship because our first son arrived in May 1979.In 1980, after completion of my fellowship, Rachel and I explored multiple options for employment in several states.A new choice emerged unexpectedly when John H. Galla, who had been a mentor throughout my fellowship and had decided to join a new nephrology group in Lexington, changed his plans.After extensive discussions, he opted to reunite with former faculty colleagues in the Division of Nephrology at UAB.I took his place in the private-practice group.Over the next two years, I encountered many patients with IgA nephropathy.Through contact with Galla, I started to participate in clinical research with Jiri Mestecky at UAB, an expert on the immunobiology of IgA. 2 DISCOVERY OF FAMILIAL IgA NEPHROPATHY IN SOUTHEASTERN KENTUCKY, USA In 1982, I biopsied a patient with IgA nephropathy who then informed me about her first cousin with the disease whom I had biopsied about six months earlier.Several other relatives had undefined kidney disease.Over the next year, I worked with Wyatt, Patricia A.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.010 | 0.039 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".