Oxford-MEST classification in IgAnephropathy patinets: A report from Iran
Bibliographic record
Abstract
BACKGROUND: There is a limited knowledge about the morphological features of IgA nephropathy (IgAN)in the middle east region. OBJECTIVES: The objective of this study was to evaluate the spectrum of histopathological findings in IgAN patients at our laboratory. PATIENTS AND METHODS: At this work, an observational study reported which was conducted on IgAN patients using the Oxford-MEST classification system. RESULTS: In this survey, of 102 patients 71.6 % were male. The mean age of the patients was 37.7 ± 13.6 years. Morphologic variables of MEST classification was as follows; M1: 90.2 %, E: 32 %, S: 67 % also,T in grads I and II were in 30% and 19% respectively, while 51% were in grade zero. A significant difference was observed in segmental glomerulosclerosis (P=0.003) and interstitial fibrosis/tubular atrophy frequency distribution (P= 0.045), between males and females . Furthermore, it was found that mesangial hypercellularity was more prevalent in yonger patients. Moreover, there was a significant correlation between serum creatinine and crescents (P<0.001). There was also significant correlation of serum creatinine with segmental glomerulosclerosis (P<0.001). CONCLUSIONS: Higher prevalence of segmental glomerulosclerosis and interstitial fibrosis/ tubular atrophy, as the two of, four variables of Oxford-MEST classification of IgAN in male patients further attests that male gender is a risk factor in this disease.In this study the significant correlation between serum creatinine and crescent was in an agreement with previous studies and suggests for the probable accomodation of extracapillary proliferation as a new variable in MEST system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".