The 2019 and 2021 International Workshops on Alport Syndrome
Bibliographic record
Abstract
In 1927 Arthur Cecil Alport, a South African physician, described a British family with an inherited form of kidney disease that affected males more severely than females and was sometimes associated with hearing loss. In 1961, the eponymous name Alport syndrome was adopted. In the late twentieth century three genes responsible for the disease were discovered: COL4A3, COL4A4, and COL4A5 encoding for the α3, α4, α5 polypeptide chains of type IV collagen, respectively. These chains assemble to form heterotrimers of type IV collagen in the glomerular basement membrane. Scientists, clinicians, patient representatives and their families, and pharma companies attended the 2019 International Workshop on Alport Syndrome, held in Siena, Italy, from October 22 to 26, and the 2021 online Workshop from November 30 to December 4. The main topics included: disease re-naming, acknowledging the need to identify an appropriate term able to reflect considerable clinical variability; a strategy for increasing the molecular diagnostic rate; genotype-phenotype correlation from monogenic to digenic forms; new therapeutics and new therapeutic approaches; and gene therapy using gene editing. The exceptional collaborative climate that was established in the magical medieval setting of Siena continued in the online workshop of 2021. Conditions were established for collaborations between leading experts in the sector, including patients and drug companies, with the aim of identifying a cure for Alport syndrome.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".