Focal segmental glomerulosclerosis: a morphologic diagnosis in evolution.
Bibliographic record
Abstract
CONTEXT: The diagnosis of focal segmental glomerulosclerosis (FSGS) is a descriptive pathologic diagnosis that in certain clinical situations (ie, primary or idiopathic) becomes its own disease. The clinical diversity, varied histology, and nonspecific morphologic features of FSGS all contribute to the complexity and problematic nature in making a pathologic diagnosis of FSGS. The definitions of the disease and of the morphologic features have evolved during the last century. OBJECTIVE: To review historic and morphologic features of FSGS in order to demonstrate a practical approach in achieving a pathologic diagnosis of FSGS on kidney tissue. DATA SOURCES: In 2004 a working proposal on the pathologic (morphologic) classification of FSGS was published in an attempt to unify the complexity of diagnosing FSGS, and it has shown to be both reproducible and with unique clinical implications for each defined FSGS variant. CONCLUSIONS: An accurate diagnosis of FSGS can be challenging. During the last few decades, numerous new scientific discoveries have enriched our knowledge of pathogenetic mechanisms of nephrotic syndrome. Thus, it is expected there will be a need for a further modification to a morphologic classification and that the pathologist's role in diagnosing FSGS will remain in evolution. This review recapitulates the history of the pathologic diagnosis of FSGS and a current morphologic classification, hopefully opening up a discussion for further modifications that reflect the status of knowledge evolving in the 21st century.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".