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Prevalence rate of Cryptosporidium infection in hemodialysis patients in Iran

2006· article· en· W2049280271 on OpenAlexvenueno aff
Shiva Seyrafian, Nader Pestehchian, Maryam Kerdegari, Hossein Ali Yousefi, Bahar Bastani

Bibliographic record

VenueHemodialysis International · 2006
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsHemodialysisMedicineCryptosporidiumIntensive care medicineInternal medicineInfection rateSurgeryMicrobiologyFecesBiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.244
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2006
Admission routes1
Has abstractyes

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