Effect of hepatitis C infection on anemia in hemodialysis patients
Bibliographic record
Abstract
Hepatitis C (HCV) infection is commonly seen in dialysis patients, but its long-term deleterious effects in these patients are unknown. We evaluated the effect of HCV infection on anemia in our hemodialysis population. This retrospective case control study was carried out from January 1999 to February 2007. The HCV positive patients were assessed for a 12-month period by quarterly lab results for the prevalence of anemia, iron stores, dialysis adequacy, and alanine aminotranferase levels. Their requirements of erythropoietin (EPO) and intravenous (IV) iron were assessed during these months of clinical stability. A control group of age-matched, race-matched, and gender-matched hemodialysis patients with no history of HCV was similarly assessed for anemia, iron stores, and EPO and IV-iron requirements. Twenty-two HCV-positive patients were included for comparison analysis with 44 control patients for 1:2 matching. The mean EPO requirement for the hepatitis group was 17,307 +/- 14,708 U/month in comparison with the control group, which required 49,134 +/- 49,375 U/month (p value <0.01). The mean dose of IV-iron was 120 +/- 143 mg/month for hepatitis patients and 163 +/- 112 mg/month in the control group (p=0.07). The patients with HCV have lower requirement of exogenous EPO replacement compared with their age-matched, gender-matched, and race-matched dialysis counterparts. The IV-iron requirement was not significantly different between the 2 groups but had a suggestive lower trend in the hepatitis group. This needs to be further studied in larger trials.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".