Five-Year study on renal outcomes in biopsy-proven focal segmental glomerulosclerosis patients in Shiraz, Iran.
Bibliographic record
Abstract
Background: Focal segmental glomerulosclerosis (FSGS) is a prevalent glomerular disease that often leads to nephrotic syndrome. It is characterized by consolidating a portion of the glomerular capillary tuft connected to Bowman's capsule. This retrospective cohort study aimed to determine the demographic characteristics, risk factors, and prognostic indicators associated with FSGS in Shiraz, Iran. Methods: The study included 53 primary FSGS patients aged over 18 years who were referred to clinics affiliated with Shiraz University of Medical Sciences. Data were collected through a comprehensive data-gathering sheet encompassing demographic information, medical history, laboratory test results, and histopathological findings. Statistical analysis was performed using SPSS 18, considering a significance level of p<0.05. Results: A five-year follow-up was conducted on the 53 patients, with the mean age of 41.0±13.3 years. The most common FSGS variants observed were "not otherwise specified" (NOS, 13.2%) and tip variant (7.5%). Older patients exhibited higher disease activity, whereas remission rates were higher among younger individuals (P=0.012). Patients achieving remission had lower creatinine and Pro/Cr ratios and higher glomerular filtration rates (p<0.05). Treatment involving a combination of corticosteroids and mycophenolate mofetil showed a significant correlation with remission (P=0.036). Conclusion: Older patients with higher creatinine levels, higher Pro/Cr ratios, and lower glomerular filtration rates at disease onset may require more aggressive treatment. Combination therapy with mycophenolate mofetil and corticosteroids yields better outcomes, leading to increased remission rates. These findings provide valuable insights for managing FSGS patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".