Prevalence rate of Cryptosporidium infection in hemodialysis patients in Iran
Bibliographic record
Abstract
Cryptosporidium is one of the most common causes of diarrhea in the world, which can be severe and prolonged in immunocompromised patients. We compared the prevalence rate of Cryptosporidium infection in hemodialysis patients and 2 control groups (i.e., their healthy family members and normal population). Stool specimens of 104 adult outpatient chronic hemodialysis patients, their 91 healthy family members, and 140 healthy individuals were examined for the presence of Cryptosporidium oocysts by using a modified acid-fast staining method. Twelve (11.5%) dialysis patients were infected with Cryptosporidium. This was significantly higher than 4 (4.4%), and 5 (3.6%) cases in the 2 control groups, respectively (p < 0.05). There was no significant difference between the 2 control groups. The prevalence rate of Cryptosporidium infection did not correlate with patients' sex, age, duration of dialysis, history of kidney transplantation, or history of taking immunosuppressive drugs. However, it was significantly higher in diabetics vs. nondiabetics (19.4% vs. 8.3%, respectively, p < 0.05). Our results indicate that the prevalence rate of Cryptosporidium infection is considerably higher in dialysis patients than in the general population. Moreover, dialyzed diabetic patients had the highest rate of infection. As hemodialysis patients are candidates for renal transplantation, general preventive measures against acquiring Cryptosporidium infection must be considered.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".