The Spectrum of Biopsy-Proved Kidney Disease: A Retrospective Single Center Study in Erbil-Iraq
Bibliographic record
Abstract
BACKGROUND Renal biopsy is crucial to determine the pattern of the different types of renal diseases. It represents the gold standard of diagnostics for renal pathologies, including glomerular diseases, and it has an important value for the prognosis, monitoring disease progression, and planning the management protocol. AIMS AND OBJECTIVE To report the frequency of different pathological lesions affecting the kidney in patients who were admitted to our medical centre. MATERIALS AND METHODS This is a retrospective study of all patients with renal diseases who underwent percutaneous renal biopsy at the Erbil Kidney Centre for eight years (1st of January 2010 to 31st of December 2017). A total of 893 cases were biopsied and subsequently studied via histopathological examination and immunofluorescence microscopy. The study is ethically permitted by the Kurdistan Board for Medical Specialization. RESULTS The average age of the patients was 30.9 years. The most common clinical indication for biopsy included nephrotic syndrome (46.47%), acute renal failure (19.04%), chronic renal failure (15.34%), nephritic syndrome (7.39%), proteinuria alone (7.28%), and hematuria alone (4.48%). In patients with a primary glomerular disease, focal segmental glomerulosclerosis and minimal change disease were the most frequent (27.44% and 16.01%) in the younger patients (18.61±13.47 years), while membranous glomerulonephritis was more common in older patients (38.94±13.69 years). Patients with a secondary glomerular disease were mainly diagnosed with lupus nephritis, amyloidosis, and diabetic nephropathy. CONCLUSION The epitome of our study signifies that the spectrum of glomerular diseases varies based on age, sex, ethnicity, and geographical distribution. The implementation of renal biopsy proved to be a cornerstone in reaching the correct diagnosis. Future studies should implement the use of electron microscopy in conjunction with classical techniques of histopathology and immunofluorescence microscopy to diagnose equivocal cases of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".