Isolated renal hydatid disease in a non-endemic country: a single centre experience
Bibliographic record
Abstract
Objective: Isolated renal hydatid disease (HD) is rare in nonendemic countries. Clinical and radiological suspicion warrants appropriate serological tests, preoperative treatment and intra-operative precautions. We present a tertiary care centre experience of isolated renal HD in a non-endemic country.Methods: We reviewed the medical records of patients with HD treated in the past 20 years. We identified patients with the definitive diagnosis of isolated renal HD and described their management.Results: Of the 119 cases with HD, 6 were found to have isolated renal involvement (5%). Their median age was 46.5 (28-70) years. Five patients presented with flank pain and 1 had an incidentally discovered renal mass. Radiologic investigations raised the suspicionof possible HD in 4 cases, while 2 cases were diagnosed as renal tumours. Computerized tomography showed complex renal cyst in 4, solid renal mass with heterogonous enhancement in 2 and calcification in 5. Eosinophilia and indirect hemagglutinationtest (IHA) were positive in 3 of the 4 suspected cases. Three cases were treated as renal tumours, while 3 were managed as HD. Four cases had total nephrectomy and 2 had partial nephrectomy. Histopathology revealed that all cases had renal HD. Patients were followed for a median of 7.3 (0.4-11.3) years with no evidence of recurrence.Conclusions: Isolated renal HD is a challenging preoperative diagnosis in non-endemic countries. The definitive diagnosis is only possible by histopathology. Retrospectively, HD mimicked renal tumours in half the cases and should be considered in the differential diagnosis of renal space occupying lesions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".